1. Import packages and load data¶

In [2]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn
import xgboost as xgb
from xgboost import XGBClassifier

# import logistic regression libraries
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay, roc_curve, roc_auc_score, RocCurveDisplay, auc

# import classification tree libraries
from sklearn.tree import DecisionTreeClassifier, plot_tree

# import random forest libraries
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import BaggingClassifier

# import cross validation libraries
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from sklearn.preprocessing import StandardScaler
In [3]:
# load data
df = pd.read_csv(r"C:\Users\woowe\Downloads\hospital_readmissions.csv")
print(df.head())

       age  time_in_hospital  n_lab_procedures  n_procedures  n_medications  \
0  [70-80)                 8                72             1             18   
1  [70-80)                 3                34             2             13   
2  [50-60)                 5                45             0             18   
3  [70-80)                 2                36             0             12   
4  [60-70)                 1                42             0              7   

   n_outpatient  n_inpatient  n_emergency medical_specialty       diag_1  \
0             2            0            0           Missing  Circulatory   
1             0            0            0             Other        Other   
2             0            0            0           Missing  Circulatory   
3             1            0            0           Missing  Circulatory   
4             0            0            0  InternalMedicine        Other   

        diag_2       diag_3 glucose_test A1Ctest change diabetes_med  \
0  Respiratory        Other           no      no     no          yes   
1        Other        Other           no      no     no          yes   
2  Circulatory  Circulatory           no      no    yes          yes   
3        Other     Diabetes           no      no    yes          yes   
4  Circulatory  Respiratory           no      no     no          yes   

  readmitted  
0         no  
1         no  
2        yes  
3        yes  
4         no

Exploratory Data Analysis¶

Data quantity¶

In [6]:
print("The raw dataset has {} rows and {} columns".format(df.shape[0], df.shape[1]))

The raw dataset has 25000 rows and 17 columns

Null values¶

In [8]:
# check null values
missing_counts = df.isnull().sum()
print("Count of Missing Values")
print(missing_counts)

Count of Missing Values
age                  0
time_in_hospital     0
n_lab_procedures     0
n_procedures         0
n_medications        0
n_outpatient         0
n_inpatient          0
n_emergency          0
medical_specialty    0
diag_1               0
diag_2               0
diag_3               0
glucose_test         0
A1Ctest              0
change               0
diabetes_med         0
readmitted           0
dtype: int64

Data types¶

In [10]:
# check data types
df.dtypes
Out[10]:
age                  object
time_in_hospital      int64
n_lab_procedures      int64
n_procedures          int64
n_medications         int64
n_outpatient          int64
n_inpatient           int64
n_emergency           int64
medical_specialty    object
diag_1               object
diag_2               object
diag_3               object
glucose_test         object
A1Ctest              object
change               object
diabetes_med         object
readmitted           object
dtype: object

Univariate Distribution¶

In [12]:
# EDA on the entire raw dataset
# Define numeric and categorical columns for EDA
num_columns = df.select_dtypes(include=['int64']).columns.tolist()
cat_columns = df.select_dtypes(include=['object']).columns.tolist()

# Plot numeric feature distributions
plt.figure(figsize=(30, 15))
for i, column in enumerate(num_columns, 1):
    plt.subplot(4, 5, i)
    sns.histplot(df[column], kde=False)
    plt.title(column)
plt.tight_layout()
plt.show()

# Plot categorical feature distributions
plt.figure(figsize=(30, 5))
for i, column in enumerate(cat_columns, 1):
    plt.subplot(1, len(cat_columns), i)
    sns.countplot(x=column, data=df)
    plt.title(column)
plt.tight_layout()
plt.show()
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Summary table¶

In [14]:
# summary table of dataset
df.describe()
Out[14]:
time_in_hospital n_lab_procedures n_procedures n_medications n_outpatient n_inpatient n_emergency
count 25000.00000 25000.00000 25000.000000 25000.000000 25000.000000 25000.000000 25000.000000
mean 4.45332 43.24076 1.352360 16.252400 0.366400 0.615960 0.186600
std 3.00147 19.81862 1.715179 8.060532 1.195478 1.177951 0.885873
min 1.00000 1.00000 0.000000 1.000000 0.000000 0.000000 0.000000
25% 2.00000 31.00000 0.000000 11.000000 0.000000 0.000000 0.000000
50% 4.00000 44.00000 1.000000 15.000000 0.000000 0.000000 0.000000
75% 6.00000 57.00000 2.000000 20.000000 0.000000 1.000000 0.000000
max 14.00000 113.00000 6.000000 79.000000 33.000000 15.000000 64.000000

Check outliers¶

In [16]:
# check outliers
for column in df.select_dtypes(include=['number']).columns:
    plt.figure(figsize=(10, 6))  # Set figure size for each plot
    sns.boxplot(x=df[column])  # Create boxplot
    plt.title(f"Boxplot of {column}", fontdict={'fontsize': 20})  # Set title
    plt.xlabel(column)  # Label x-axis
    plt.show()  # Display the plot
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We noted that there were many outliers for n_lab_procedures, n_medications, n_outpatient, n_inpatient and n_emergency. However, since they are not data entry mistakes and are valid values, we decided to keep them.

2. Feature Engineering¶

In [19]:
# Create new interaction term 
df['severity'] = df['time_in_hospital'] * (df['n_lab_procedures'] + 32 * df['n_procedures'])
In [20]:
# Log transform skewed variables
df['log_time_in_hospital'] = np.log1p(df['time_in_hospital'])
df['log_n_procedures'] = np.log1p(df['n_procedures'])
df['log_n_inpatient'] = np.log1p(df['n_inpatient'])
df['log_severity'] = np.log1p(df['severity'])
In [21]:
# Drop the original columns to prevent multicollinearity issues
df.drop(["time_in_hospital", "n_procedures", "n_inpatient", "severity"], axis = 1, inplace = True)
In [22]:
df.isnull().sum()
Out[22]:
age                     0
n_lab_procedures        0
n_medications           0
n_outpatient            0
n_emergency             0
medical_specialty       0
diag_1                  0
diag_2                  0
diag_3                  0
glucose_test            0
A1Ctest                 0
change                  0
diabetes_med            0
readmitted              0
log_time_in_hospital    0
log_n_procedures        0
log_n_inpatient         0
log_severity            0
dtype: int64

3. Data Pre-processing¶

In [57]:
# one-hot encode independent variables
df_encoded = pd.get_dummies(df, columns=[
    'age', 'medical_specialty', 'diag_1', 'diag_2', 'diag_3', 'glucose_test', 'A1Ctest', 'change', 'diabetes_med'
], drop_first=True)
In [59]:
# remove '[' symbol in age values [70-80) since it caused problems for decision trees
df_encoded.columns = df_encoded.columns.str.replace('[', '')
In [61]:
# one-hot readmission variable
df_new = pd.get_dummies(df_encoded, columns = ['readmitted'], drop_first = True, dtype = int)
df_new.head()
Out[61]:
n_lab_procedures n_medications n_outpatient n_emergency log_time_in_hospital log_n_procedures log_n_inpatient log_severity age_50-60) age_60-70) ... diag_3_Musculoskeletal diag_3_Other diag_3_Respiratory glucose_test_no glucose_test_normal A1Ctest_no A1Ctest_normal change_yes diabetes_med_yes readmitted_yes
0 72 18 2 0 2.197225 0.693147 0.0 6.725034 False False ... False True False True False True False False True 0
1 34 13 0 0 1.386294 1.098612 0.0 5.686975 False False ... False True False True False True False False True 0
2 45 18 0 0 1.791759 0.000000 0.0 5.420535 True False ... False False False True False True False True True 1
3 36 12 1 0 1.098612 0.000000 0.0 4.290459 False False ... False False False True False True False True True 1
4 42 7 0 0 0.693147 0.000000 0.0 3.761200 False True ... False False True True False True False False True 0

5 rows × 47 columns

In [63]:
# Splitting the dataset into features (X) and target (y)
X = df_new.drop('readmitted_yes', axis=1)
y = df_new['readmitted_yes']
In [65]:
# Splitting into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

4. Model Training¶

In [68]:
cv = StratifiedKFold(n_splits=10, shuffle = True, random_state = 42)
In [70]:
# Report GridSearchCV results
def report_GridSearchCV_results(grid):
    print("- Best combination of hyperparameters:\n", grid.best_params_, "\n")
    print("- Best mean_test_score:\n", grid.best_score_, "\n")
    
    scores = []
    for i in range(grid.n_splits_):
        scores.append(grid.cv_results_['split{}_test_score'.format(i)][grid.best_index_])
    print("- Score by fold for best estimator:\n", scores, "\n")
    
    # View top 10 hyperparameter combinations by mean_test_score (mean AUC on validation set)
    print("- Top 10 hyperparameter combinations by mean_test_score:")
    display(pd.DataFrame(grid.cv_results_)[["rank_test_score", "mean_test_score"] 
                                            + ["param_" + param for param in grid.param_grid]]\
              .sort_values(by = "mean_test_score", ascending = False)\
              .set_index("rank_test_score").head(10))
    
    return None
In [72]:
# Compare training dataset performance vs validation dataset performance
def compare_performance(grid):  
    # retrieve training and validation scores
    train_scores=grid.cv_results_['mean_train_score']
    val_scores=grid.cv_results_['mean_test_score']

    # limit to 10 rows
    train_scores_limited=train_scores[:10]
    val_scores_limited=val_scores[:10]
    
    # create dataframe to store scores
    all_scores=pd.DataFrame({
    "train_AUC": train_scores_limited, 
    "val_AUC": val_scores_limited
    }, index=range(1,11))

    mean_scores=pd.DataFrame({
        "train_AUC": [train_scores_limited.mean()],
        "val_AUC": [val_scores_limited.mean()]
    }, index=["Mean"])

    all_scores_combined=pd.concat([all_scores, mean_scores])
    
    return all_scores_combined
In [74]:
# evaluate model on test set
def evaluate_model(best_model, X_test_scaled, y_test): 
    """
    Parameters: 
    - best_model: The best estimator from grid search
    - X_test_scaled: Scaled test data
    - y_test: True labels for test set
    """
    # predict probabilities and labels
    y_prob=best_model.predict_proba(X_test_scaled)[:,1]
    y_pred=best_model.predict(X_test_scaled)

    # metrics
    test_auc=roc_auc_score(y_test, y_prob)
    accuracy=accuracy_score(y_test, y_pred)
    conf_matrix=confusion_matrix(y_test, y_pred)
    classification_rep=classification_report(y_test, y_pred)

    # print metrics
    print(f"Test AUC: {test_auc:.2f}")
    print(f'Accuracy: {accuracy:.2f}')
    print('Confusion Matrix:'); print(conf_matrix)
    disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)
    disp.plot()
    plt.show()
    print('Classification Report:')
    print(classification_rep)
In [76]:
# Plot ROC curve on test set
def plot_roc_curve(best_model, X_test_scaled, y_test):
    """
    Parameters:
    - best_model: The best estimator from grid search
    - X_test_scaled: Scaled test features
    - y_test: True labels for the test set
    """
    # predict probabilities
    y_prob=best_model.predict_proba(X_test_scaled)[:,1]

    # compute roc curve
    fpr, tpr, thresholds= roc_curve(y_test, y_prob)
    roc_auc=auc(fpr, tpr)

    # plot roc curve
    plt.figure(figsize=(8, 6))
    plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'AUC = {roc_auc:.2f}')
    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver Operating Characteristic (ROC) Curve')
    plt.legend(loc='lower right')
    plt.show()
In [78]:
# Plot average feature importance across CV folds
def plot_feature_importance_chart(model, X_train, y_train, cv, model_name):
    train_predictions_std = [] 
    feature_importances = []

    # Iterate over each fold
    for train_idx, val_idx in cv.split(X_train, y_train):
        X_train_cv, X_val_cv = X_train.iloc[train_idx], X_train.iloc[val_idx]
        y_train_cv, y_val_cv = y_train.iloc[train_idx], y_train.iloc[val_idx]

        # Fit model on current fold
        model.fit(X_train_cv, y_train_cv)

        # Store feature importances for this fold
        feature_importances.append(model.feature_importances_)
    
        val_pred_proba = model.predict_proba(X_val_cv)[:, 1]
        train_predictions_std.append(np.std(val_pred_proba))
    
    avg_feature_importance = np.mean(feature_importances, axis=0)
    
    # Sort features by importance (descending order)
    feature_names = X_train.columns
    sorted_indices = np.argsort(avg_feature_importance)[::-1]  # Descending order
    sorted_importances = avg_feature_importance[sorted_indices]
    sorted_feature_names = feature_names[sorted_indices]
    
    # Plot the sorted feature importances
    plt.figure(figsize=(10, 8))
    plt.barh(sorted_feature_names, sorted_importances, color='skyblue')
    plt.xlabel("Average Coefficient (Feature Importance)")
    plt.title("Average Feature Importance Across Cross-Validation Folds")
    plt.gca().invert_yaxis()  # Highest importance at the top
    plt.grid(axis='x', linestyle='--', alpha=0.7)
    plt.tight_layout()
    plt.show()

Classification Tree¶

Pre-pruning¶

In [82]:
# Initialize model
classificationtree = DecisionTreeClassifier(random_state=42)

# Define the hyperparameter grid
clf_param_grid = {
    'max_depth': [2, 3, 4],
    'min_samples_leaf': [500, 1000, 2000],
    'max_leaf_nodes': [None, 5, 10, 15]
}

# Create a GridSearchCV object
grid_search_clf = GridSearchCV(estimator=classificationtree, param_grid=clf_param_grid, cv=cv, scoring='roc_auc', verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_clf.fit(X_train, y_train)

Fitting 10 folds for each of 36 candidates, totalling 360 fits
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.614, test=0.614) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.616, test=0.595) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.613, test=0.622) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.614, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.615, test=0.616) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.614, test=0.627) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.615, test=0.604) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.614, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.615, test=0.617) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.614, test=0.614) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.616, test=0.595) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.613, test=0.622) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.614, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.615, test=0.616) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.614, test=0.627) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.615, test=0.604) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.614, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.615, test=0.617) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.616, test=0.611) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.618, test=0.599) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.616, test=0.615) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.615, test=0.622) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.617, test=0.609) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.614, test=0.614) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.616, test=0.595) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.613, test=0.622) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.614, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.615, test=0.616) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.614, test=0.627) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.615, test=0.604) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.614, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.615, test=0.617) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.614, test=0.614) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.616, test=0.595) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.613, test=0.622) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.614, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.615, test=0.616) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.614, test=0.627) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.615, test=0.604) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.614, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.615, test=0.617) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.616, test=0.611) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.618, test=0.599) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.616, test=0.615) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.615, test=0.622) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.617, test=0.609) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.614, test=0.614) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.616, test=0.595) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.613, test=0.622) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.614, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.615, test=0.616) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.614, test=0.627) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.615, test=0.604) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.614, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.615, test=0.617) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.614, test=0.614) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.616, test=0.595) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.613, test=0.622) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.614, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.615, test=0.616) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.614, test=0.627) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.615, test=0.604) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.614, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.615, test=0.617) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.616, test=0.611) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.618, test=0.599) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.616, test=0.615) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.615, test=0.622) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.617, test=0.609) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.614, test=0.614) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.616, test=0.595) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.613, test=0.622) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.614, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.615, test=0.616) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.614, test=0.627) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.615, test=0.604) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.614, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.615, test=0.617) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.614, test=0.614) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.616, test=0.595) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.613, test=0.622) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.614, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.615, test=0.616) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.614, test=0.627) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.615, test=0.604) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.614, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.615, test=0.617) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.616, test=0.611) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.618, test=0.599) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.616, test=0.615) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.615, test=0.622) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.617, test=0.609) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.628, test=0.616) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.628, test=0.614) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.625, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.627, test=0.629) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.629, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.622, test=0.613) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.629, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.627, test=0.618) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.628, test=0.612) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.625, test=0.637) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.626, test=0.630) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.627, test=0.631) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.627, test=0.638) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.628, test=0.615) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.626, test=0.631) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.628, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.628, test=0.626) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.622, test=0.619) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.603) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.621, test=0.621) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.621, test=0.626) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.620, test=0.633) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.620, test=0.635) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.620, test=0.631) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.621, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.622, test=0.612) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.615) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.608) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.623, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.624, test=0.626) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.631) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.624, test=0.634) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.610) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.619, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.626, test=0.622) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.615) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.608) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.623, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.624, test=0.626) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.631) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.624, test=0.634) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.610) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.623, test=0.628) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.626, test=0.622) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.622, test=0.619) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.623, test=0.603) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.621) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.626) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.633) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.635) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.623, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.631) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.622, test=0.612) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.628, test=0.616) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.628, test=0.614) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.625, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.627, test=0.629) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.629, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.622, test=0.613) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.629, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.627, test=0.618) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.628, test=0.612) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.625, test=0.637) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.626, test=0.630) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.627, test=0.631) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.627, test=0.638) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.628, test=0.615) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.626, test=0.631) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.628, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.628, test=0.626) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.622, test=0.619) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.603) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.621, test=0.621) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.621, test=0.626) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.620, test=0.633) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.620, test=0.635) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.620, test=0.631) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.621, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.622, test=0.612) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.628, test=0.616) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.628, test=0.614) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.625, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.627, test=0.629) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.629, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.622, test=0.613) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.629, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.627, test=0.618) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.628, test=0.612) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.625, test=0.637) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.626, test=0.630) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.627, test=0.631) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.627, test=0.638) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.628, test=0.615) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.626, test=0.631) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.628, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.628, test=0.626) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.622, test=0.619) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.603) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.621, test=0.621) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.621, test=0.626) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.620, test=0.633) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.620, test=0.635) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.620, test=0.631) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.621, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.622, test=0.612) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.635, test=0.624) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.636, test=0.614) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.632, test=0.643) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.635, test=0.637) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.637, test=0.608) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.632, test=0.633) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.636, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.634, test=0.621) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.635, test=0.612) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.632, test=0.643) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.633, test=0.635) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.632, test=0.637) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.631, test=0.639) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.635, test=0.615) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.632, test=0.633) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.633, test=0.631) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.634, test=0.627) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.624, test=0.621) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.626, test=0.604) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.624, test=0.627) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.637) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.626, test=0.608) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.625, test=0.618) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.615) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.608) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.623, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.624, test=0.626) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.631) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.624, test=0.634) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.610) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.619, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.626, test=0.622) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.615) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.608) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.623, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.624, test=0.626) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.631) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.624, test=0.634) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.610) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.623, test=0.628) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.626, test=0.622) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.622, test=0.619) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.623, test=0.603) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.621) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.626) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.633) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.635) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.623, test=0.605) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.631) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.629) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.622, test=0.612) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.635, test=0.624) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.636, test=0.614) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.632, test=0.643) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.634, test=0.636) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.633, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.632, test=0.639) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.636, test=0.610) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.632, test=0.633) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.635, test=0.625) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.634, test=0.621) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.635, test=0.612) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.632, test=0.643) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.633, test=0.635) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.632, test=0.637) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.631, test=0.639) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.635, test=0.615) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.632, test=0.633) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.633, test=0.631) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.634, test=0.627) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.624, test=0.621) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.626, test=0.604) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.624, test=0.627) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.637) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.626, test=0.608) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.625, test=0.618) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.635, test=0.624) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.636, test=0.614) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.632, test=0.643) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.635, test=0.637) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.637, test=0.608) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.632, test=0.633) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.636, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.634, test=0.621) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.635, test=0.612) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.632, test=0.643) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.633, test=0.635) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.632, test=0.637) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.631, test=0.639) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.635, test=0.615) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.632, test=0.633) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.633, test=0.631) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.634, test=0.627) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.624, test=0.621) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.626, test=0.604) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.624, test=0.627) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.637) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.626, test=0.608) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.625, test=0.618) total time=   0.0s
Out[82]:
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'max_depth': [2, 3, 4],
                         'max_leaf_nodes': [None, 5, 10, 15],
                         'min_samples_leaf': [500, 1000, 2000]},
             return_train_score=True, scoring='roc_auc', verbose=4)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'max_depth': [2, 3, 4],
                         'max_leaf_nodes': [None, 5, 10, 15],
                         'min_samples_leaf': [500, 1000, 2000]},
             return_train_score=True, scoring='roc_auc', verbose=4)
DecisionTreeClassifier(max_depth=4, min_samples_leaf=1000, random_state=42)
DecisionTreeClassifier(max_depth=4, min_samples_leaf=1000, random_state=42)
In [84]:
report_GridSearchCV_results(grid_search_clf)

- Best combination of hyperparameters:
 {'max_depth': 4, 'max_leaf_nodes': None, 'min_samples_leaf': 1000} 

- Best mean_test_score:
 0.6295579764334001 

- Score by fold for best estimator:
 [0.6211053235053234, 0.6124514332514333, 0.6434312858312857, 0.6353677313677314, 0.6369251433251433, 0.6393054873054873, 0.6153842122365124, 0.6330801966395186, 0.6313166252633565, 0.6272123256082094] 

- Top 10 hyperparameter combinations by mean_test_score:
mean_test_score param_max_depth param_min_samples_leaf param_max_leaf_nodes
rank_test_score
1 0.629558 4 1000 15
1 0.629558 4 1000 10
1 0.629558 4 1000 None
4 0.629480 4 500 10
5 0.629131 4 500 15
5 0.629131 4 500 None
7 0.626659 3 1000 None
7 0.626659 3 1000 15
7 0.626659 3 1000 10
10 0.624173 4 2000 15
In [86]:
compare_performance(grid_search_clf)
Out[86]:
train_AUC val_AUC
1 0.614551 0.613645
2 0.614551 0.613645
3 0.615819 0.615886
4 0.614551 0.613645
5 0.614551 0.613645
6 0.615819 0.615886
7 0.614551 0.613645
8 0.614551 0.613645
9 0.615819 0.615886
10 0.614551 0.613645
Mean 0.614932 0.614317
In [88]:
best_model_clf=grid_search_clf.best_estimator_
In [94]:
plot_feature_importance_chart(best_model_clf, X_train, y_train, cv, "Pre-pruned Classification Tree")
No description has been provided for this image
In [101]:
# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_clf, filled=True, feature_names=X_train.columns, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()
No description has been provided for this image

Create interaction term 'severity'¶

  • The new column severity reflects the interaction between time spent in the hospital and the combined number of lab and medical procedures.
  • A higher value of severity could indicate a more serious condition, as it implies that patients with longer hospital stays and more procedures are likely to have greater health complexities.
  • The term 32 * df['n_procedures'] adds a weighted contribution of the number of procedures, suggesting that each procedure has a significant impact on the overall severity.
In [ ]:
# create interaction term 'severity'
df_new = df.copy()
df_new['severity'] = df_new['time_in_hospital'] * (df_new['n_lab_procedures'] + 32 * df_new['n_procedures'])

Transform existing variables¶

  • For time_in_hospital which is right-skewed, we apply a regular natural log transform: The transformed variable is named log_time_in_hospital.

  • For n_procedures which is right-skewed, we apply a regular natural log transform: The transformed variable is named log_n_procedures.

  • For n_inpatient which is right-skewed, we apply a regular natural log transform: The transformed variable is named log_n_inpatient.

In [ ]:
# Apply a log transform to existing variables
df_new["log_time_in_hospital"] = np.log1p(df_new["time_in_hospital"])
df_new["log_n_procedures"] = np.log1p(df_new["n_procedures"])
df_new["log_n_inpatient"] = np.log1p(df_new["n_inpatient"])

# Drop the original columns to prevent multicollinearity issues
df_new.drop(["time_in_hospital", "n_procedures", "n_inpatient"], axis = 1, inplace = True)
In [ ]:
df_new.head()

Exploratory Data Analysis (Processed Data)¶

Data types¶

In [ ]:
# check data types
df_new.dtypes

Univariate Distribution¶

In [ ]:
# EDA on the entire raw dataset
# Define numeric and categorical columns for EDA
num_columns_new = df_new.select_dtypes(include=['int64', 'float64']).columns.tolist()
cat_columns_new = df_new.select_dtypes(include=['object']).columns.tolist()

# Plot numeric feature distributions
plt.figure(figsize=(30, 15))
for i, column in enumerate(num_columns_new, 1):
    plt.subplot(4, 5, i)
    sns.histplot(df_new[column], kde=False)
    plt.title(column)
plt.tight_layout()
plt.show()

# Plot categorical feature distributions
plt.figure(figsize=(30, 5))
for i, column in enumerate(cat_columns_new, 1):
    plt.subplot(1, len(cat_columns_new), i)
    sns.countplot(x=column, data=df)
    plt.title(column)
plt.tight_layout()
plt.show()

Summary table¶

In [ ]:
# summary table of dataset
df_new.describe()

Check outliers¶

In [ ]:
# check outliers
for column in df_new.select_dtypes(include=['number']).columns:
    plt.figure(figsize=(10, 6))  # Set figure size for each plot
    sns.boxplot(x=df_new[column])  # Create boxplot
    plt.title(f"Boxplot of {column}", fontdict={'fontsize': 20})  # Set title
    plt.xlabel(column)  # Label x-axis
    plt.show()  # Display the plot
In [ ]:
# one-hotting independent variables
df2 = pd.get_dummies(df_new, columns = ['diabetes_med'], drop_first = True, dtype = int)
df3 = pd.get_dummies(df2, columns = ['age'], drop_first = True, dtype = int)
df4 = pd.get_dummies(df3, columns = ['medical_specialty'], drop_first = True, dtype = int)
df5 = pd.get_dummies(df4, columns = ['diag_1'], drop_first = True, dtype = int)
df6 = pd.get_dummies(df5, columns = ['diag_2'], drop_first = True, dtype = int)
df7 = pd.get_dummies(df6, columns = ['diag_3'], drop_first = True, dtype = int)
df8 = pd.get_dummies(df7, columns = ['glucose_test'], drop_first = True, dtype = int)
df9 = pd.get_dummies(df8, columns = ['A1Ctest'], drop_first = True, dtype = int)
df10 = pd.get_dummies(df9, columns = ['change'], drop_first = True, dtype = int)

# one-hot readmission variable
df11 = pd.get_dummies(df10, columns = ['readmitted'], drop_first = True, dtype = int)
print(df11.head())
In [ ]:
# define feature names for x and y datasets
# remove '[' for xgboost model to run successfully
y_name=['readmitted_yes']
x_name=['n_lab_procedures', 'n_medications', 'n_outpatient', 'n_emergency',
       'severity', 'log_time_in_hospital', 'log_n_procedures',
       'log_n_inpatient', 'diabetes_med_yes', 'age_50-60)', 'age_60-70)',
       'age_70-80)', 'age_80-90)', 'age_90-100)',
       'medical_specialty_Emergency/Trauma',
       'medical_specialty_Family/GeneralPractice',
       'medical_specialty_InternalMedicine', 'medical_specialty_Missing',
       'medical_specialty_Other', 'medical_specialty_Surgery',
       'diag_1_Diabetes', 'diag_1_Digestive', 'diag_1_Injury',
       'diag_1_Missing', 'diag_1_Musculoskeletal', 'diag_1_Other',
       'diag_1_Respiratory', 'diag_2_Diabetes', 'diag_2_Digestive',
       'diag_2_Injury', 'diag_2_Missing', 'diag_2_Musculoskeletal',
       'diag_2_Other', 'diag_2_Respiratory', 'diag_3_Diabetes',
       'diag_3_Digestive', 'diag_3_Injury', 'diag_3_Missing',
       'diag_3_Musculoskeletal', 'diag_3_Other', 'diag_3_Respiratory',
       'glucose_test_no', 'glucose_test_normal', 'A1Ctest_no',
       'A1Ctest_normal', 'change_yes']
In [ ]:
# define features (X) and target (y)
df_X = df11.iloc[:, :-1] # drop 'readmitted_yes'
df_y = df11.iloc[:, -1:] # only store 'readmitted_yes'

# split data points (rows) into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df_X, df_y, test_size = 0.2, random_state = 42)

# Scale the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Convert scaled features back to DataFrame
df_X_train_scaled = pd.DataFrame(X_train_scaled, columns=x_name).reset_index(drop=True)
df_X_test_scaled = pd.DataFrame(X_test_scaled, columns=x_name).reset_index(drop=True)

df_y_train = pd.DataFrame(y_train, columns=y_name).reset_index(drop=True)
df_y_test = pd.DataFrame(y_test, columns=y_name).reset_index(drop=True)

df_train = pd.concat([df_X_train_scaled, df_y_train], axis = 1)
df_test = pd.concat([df_X_test_scaled, df_y_test], axis = 1)

To check any null values in final datasets

In [ ]:
df_train.isnull().sum()
In [ ]:
df_test.isnull().sum()

K-fold cross validation is used since there is no class imbalance issue for our target variable.

In [ ]:
# K-fold CV splitter
kf10 = KFold(n_splits = 10, shuffle = True, random_state = 42)
In [ ]:
# Manually generate CV folds
def get_CV_folds(df, y, X, cv):
    train, val = [], []
    for train_index, val_index in cv.split(df[X], df[y]):
        train.append(df.loc[train_index])
        val.append(df.loc[val_index])
    return train, val
In [ ]:
# Report GridSearchCV results
def report_GridSearchCV_results(grid):
    print("- Best combination of hyperparams:\n", grid.best_params_, "\n")
    print("- Best mean_test_score:\n", grid.best_score_, "\n")
    
    scores = []
    for i in range(grid.n_splits_):
        scores.append(grid.cv_results_['split{}_test_score'.format(i)][grid.best_index_])
    print("- Score by fold for best estimator:\n", scores, "\n")
    
    # View top 10 hyperparameter combinations by mean_test_score (mean AUC on validation set)
    print("- Top 10 hyperparameter combinations by mean_test_score:")
    display(pd.DataFrame(grid.cv_results_)[["rank_test_score", "mean_test_score"] 
                                            + ["param_" + param for param in grid.param_grid]]\
              .sort_values(by = "mean_test_score", ascending = False)\
              .set_index("rank_test_score").head(10))
    
    return None
In [ ]:
# Compare training dataset performance vs validation dataset performance
def compare_performance(grid):  
    # retrieve training and validation scores
    train_scores=grid.cv_results_['mean_train_score']
    val_scores=grid.cv_results_['mean_test_score']

    # limit to 10 rows
    train_scores_limited=train_scores[:10]
    val_scores_limited=val_scores[:10]
    
    # create dataframe to store scores
    all_scores=pd.DataFrame({
    "train_AUC": train_scores_limited, 
    "val_AUC": val_scores_limited
    }, index=range(1,11))

    mean_scores=pd.DataFrame({
        "train_AUC": [train_scores_limited.mean()],
        "val_AUC": [val_scores_limited.mean()]
    }, index=["Mean"])

    all_scores_combined=pd.concat([all_scores, mean_scores])
    
    return all_scores_combined
In [ ]:
# Plot histogram of SD of P(Readmitted) when training set varies
def plot_probability_std(estimator, df, y, X, cv, model_name):
    train, val = get_CV_folds(df, y, X, cv)
    test = val[-1]
    prob = pd.DataFrame() 
    
    for i in range(cv.n_splits - 1):
        train = val[i]
        estimator = estimator.fit(train[X], train[y])
        prob["Fold {}".format(i+1)] = [pred[1] for pred in estimator.predict_proba(test[X])]
        
    prob_std = prob.apply(lambda x: x.std(ddof=0), axis = 1)
    
    plt.figure(figsize = (8, 5))
    plt.hist(prob_std, rwidth = 0.6, bins = np.arange(0, 0.1, 0.01))
    plt.title("{} || SD of probability predictions when training set varies".format(model_name), fontsize = 14)
    plt.ylabel("Count of test observations", fontsize = 12)
    plt.xlabel("SD of P(Readmitted)", fontsize = 12)
    plt.show()
    
    return None
In [ ]:
# Plot average feature importance across CV folds
def plot_avg_feature_importance(tree, df, y, X, cv, model_name):
    
    train, val = get_CV_folds(df, y, X, cv)
    impt = pd.DataFrame()
    
    for i in range(cv.n_splits):
        df_train = train[i]
        tree = tree.fit(df_train[X], df_train[y])
        impt[str(i)] = tree.feature_importances_
        
    ft = list(zip(X, impt.mean(axis = 1)))
    ft.sort(key = lambda x: x[1])
    plt.figure(figsize = (8, 10))
    features, importances = [x[0] for x in ft], [x[1] for x in ft]
    plt.barh(features, importances)
    plt.title("{} || Avg. feature importance across CV folds".format(model_name), fontsize = 14)
    plt.show()    

    return None
In [ ]:
# evaluate model on test set
def evaluate_model(best_model, X_test_scaled, y_test): 
    """
    Parameters: 
    - best_model: The best estimator from grid search
    - X_test_scaled: Scaled test data
    - y_test: True labels for test set
    """
    # predict probabilities and labels
    y_prob=best_model.predict_proba(X_test_scaled)[:,1]
    y_pred=best_model.predict(X_test_scaled)

    # metrics
    test_auc=roc_auc_score(y_test, y_prob)
    accuracy=accuracy_score(y_test, y_pred)
    conf_matrix=confusion_matrix(y_test, y_pred)
    classification_rep=classification_report(y_test, y_pred)

    # print metrics
    print(f"Test AUC: {test_auc:.2f}")
    print(f'Accuracy: {accuracy:.2f}')
    print('Confusion Matrix:'); print(conf_matrix)
    disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)
    disp.plot()
    plt.show()
    print('Classification Report:')
    print(classification_rep)
In [ ]:
# Plot ROC curve on test set
def plot_roc_curve(best_model, X_test_scaled, y_test):
    """
    Parameters:
    - best_model: The best estimator from grid search
    - X_test_scaled: Scaled test features
    - y_test: True labels for the test set
    """
    # predict probabilities
    y_prob=best_model.predict_proba(X_test_scaled)[:,1]

    # compute roc curve
    fpr, tpr, thresholds= roc_curve(y_test, y_prob)
    roc_auc=auc(fpr, tpr)

    # plot roc curve
    plt.figure(figsize=(8, 6))
    plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'AUC = {roc_auc:.2f}')
    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver Operating Characteristic (ROC) Curve')
    plt.legend(loc='lower right')
    plt.show()

Classification Tree¶

Pre-Pruning¶

In [ ]:
# Initialize model
classificationtree = DecisionTreeClassifier(random_state=42)

# Define the hyperparameter grid
clf_param_grid = {
    'max_depth': [2, 3, 4],
    'min_samples_leaf': [500, 1000, 2000],
    'max_leaf_nodes': [None, 5, 10, 15]
}

# Create a GridSearchCV object
grid_search_clf = GridSearchCV(estimator=classificationtree, param_grid=clf_param_grid, cv=kf10, scoring='roc_auc', verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_clf.fit(df_train[x_name], df_train[y_name])

Model Performance¶

In [ ]:
report_GridSearchCV_results(grid_search_clf)
In [ ]:
compare_performance(grid_search_clf)
In [ ]:
best_model_clf=grid_search_clf.best_estimator_
In [ ]:
plot_probability_std(best_model_clf, df_train, y_name, x_name, kf10, "Pre-pruned Classification Tree")
In [ ]:
plot_avg_feature_importance(best_model_clf, df_train, y_name, x_name, kf10, "Pre-pruned Classification Tree")
In [ ]:
evaluate_model(best_model_clf, df_X_test_scaled, df_y_test)
In [ ]:
plot_roc_curve(best_model_clf, df_X_test_scaled, df_y_test)
In [ ]:
# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_clf, filled=True, feature_names=x_name, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()

Post-pruning¶

In [ ]:
# Get effective alphas for pruning
path = classificationtree.cost_complexity_pruning_path(X_train_scaled, y_train)
ccp_alphas=path.ccp_alphas
impurities=path.impurities

# Define the hyperparameter grid
post_prune_param_grid = {
    'ccp_alpha': ccp_alphas
}

# Create a GridSearchCV object
grid_search_post_prune = GridSearchCV(estimator=classificationtree, param_grid=post_prune_param_grid, scoring='roc_auc', cv=kf10, verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_post_prune.fit(df_train[x_name], df_train[y_name])

Model Performance¶

In [ ]:
report_GridSearchCV_results(grid_search_post_prune)
In [ ]:
compare_performance(grid_search_post_prune)
In [ ]:
best_model_post_prune=grid_search_post_prune.best_estimator_
In [ ]:
plot_probability_std(best_model_post_prune, df_train, y_name, x_name, kf10, "Post-pruned Classification Tree")
In [ ]:
plot_avg_feature_importance(best_model_post_prune, df_train, y_name, x_name, kf10, "Post-pruned Classification Tree")
In [ ]:
evaluate_model(best_model_post_prune, df_X_test_scaled, df_y_test)
In [ ]:
plot_roc_curve(best_model_post_prune, df_X_test_scaled, df_y_test)
In [ ]:
# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_post_prune, filled=True, feature_names=X_train.columns, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()

Random Forest Model (with regularization)¶

In [ ]:
# Initialize model
randomforest = RandomForestClassifier(max_depth = 6, random_state = 42, bootstrap=True)

# Define the hyperparameter grid
rf_param_grid = {
    'max_depth': [2, 3, 4],
    'min_samples_leaf': [500, 1000, 2000],
    'max_features': [2, 3], 
}

# Create a GridSearchCV object
grid_search_rf = GridSearchCV(estimator=randomforest, param_grid=rf_param_grid, cv=kf10, scoring='roc_auc', verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
# To resolve error: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of 
# y to (n_samples,), for example using ravel().return fit_method(estimator, *args, **kwargs)
grid_search_rf.fit(df_train[x_name], df_train[y_name].values.ravel())

Model Performance¶

In [ ]:
report_GridSearchCV_results(grid_search_rf)
In [ ]:
compare_performance(grid_search_rf)
In [ ]:
best_model_rf=grid_search_rf.best_estimator_
In [ ]:
plot_probability_std(best_model_rf, df_train, y_name, x_name, kf10, "Bagged Random Forest")
In [ ]:
plot_avg_feature_importance(best_model_rf, df_train, y_name, x_name, kf10, "")
In [ ]:
evaluate_model(best_model_rf, df_X_test_scaled, df_y_test)
In [ ]:
plot_roc_curve(best_model_rf, df_X_test_scaled, df_y_test)

XGBoost (With regularization)¶

In [ ]:
# Initialize model
xgb_model = xgb.XGBClassifier(random_state = 42)

# Define the hyperparameter grid
xgb_param_grid = {
    'colsample_bytree': [0.3, 0.7],
    'n_estimators': [50, 100, 200],
    'max_depth': [2, 5, 10],
    'alpha': [0, 0.1, 1], # Alpha/lasso regularisation
    'lambda': [0, 0.1, 1], # Lambda/ridge regularisation
    'learning_rate': [0.01, 0.05]    
}

# Create a GridSearchCV object
grid_search_xgb = GridSearchCV(param_grid=xgb_param_grid, estimator=xgb_model, 
                        scoring='roc_auc', cv=kf10, verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_xgb.fit(df_train[x_name], df_train[y_name])

Model Performance¶

In [ ]:
report_GridSearchCV_results(grid_search_xgb)
In [ ]:
compare_performance(grid_search_xgb)
In [ ]:
best_model_xgb=grid_search_xgb.best_estimator_
In [ ]:
plot_probability_std(best_model_xgb, df_train, y_name, x_name, kf10, "XGBoost")
In [ ]:
plot_avg_feature_importance(best_model_xgb, df_train, y_name, x_name, kf10, "XGBoost")
In [ ]:
evaluate_model(best_model_xgb, df_X_test_scaled, df_y_test)
In [ ]:
plot_roc_curve(best_model_xgb, df_X_test_scaled, df_y_test)
In [ ]:
In [104]:
evaluate_model(best_model_clf, X_test, y_test)

Test AUC: 0.63
Accuracy: 0.61
Confusion Matrix:
[[2669 1331]
 [1622 1878]]
No description has been provided for this image 
Classification Report:
              precision    recall  f1-score   support

           0       0.62      0.67      0.64      4000
           1       0.59      0.54      0.56      3500

    accuracy                           0.61      7500
   macro avg       0.60      0.60      0.60      7500
weighted avg       0.60      0.61      0.60      7500
In [105]:
plot_roc_curve(best_model_clf, X_test, y_test)
No description has been provided for this image

Post-pruning¶

In [114]:
# Get effective alphas for pruning
path = classificationtree.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas=path.ccp_alphas
impurities=path.impurities

# Define the hyperparameter grid
post_prune_param_grid = {
    'ccp_alpha': ccp_alphas
}

# Create a GridSearchCV object
grid_search_post_prune = GridSearchCV(estimator=classificationtree, param_grid=post_prune_param_grid, scoring='roc_auc', cv=cv, verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_post_prune.fit(X_train, y_train)

Fitting 10 folds for each of 2080 candidates, totalling 20800 fits
[CV 1/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.522) total time=   0.6s
[CV 8/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.537) total time=   0.5s
[CV 1/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.553) total time=   0.6s
[CV 6/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time=   0.5s
[CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time=   0.3s
[CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.543) total time=   0.5s
[CV 7/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.550) total time=   0.6s
[CV 6/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.5s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.550) total time=   0.4s
[CV 6/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.5s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.6s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.5s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.5s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.6s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.3s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.537) total time=   0.3s
[CV 3/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.543) total time=   0.5s
[CV 7/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.5s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.5s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.5s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.5s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.5s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.5s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.538) total time=   0.3s
[CV 3/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.5s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.5s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.5s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.5s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.5s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.5s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.3s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.5s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time=   0.5s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.543) total time=   0.3s
[CV 7/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.538) total time=   0.3s
[CV 3/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 10/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.538) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 10/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.3s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.3s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.3s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.3s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.3s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.3s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.3s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time=   0.6s
[CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time=   0.4s
[CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.536) total time=   0.3s
[CV 3/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.557) total time=   0.3s
[CV 9/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 10/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.6s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.5s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.6s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.536) total time=   0.3s
[CV 3/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.5s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.5s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 7/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.5s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.5s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.5s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.3s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.3s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.3s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 7/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.536) total time=   0.3s
[CV 3/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.3s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.3s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.3s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.3s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time=   0.3s
[CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time=   0.3s
[CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.544) total time=   0.3s
[CV 7/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.537) total time=   0.3s
[CV 3/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.537) total time=   0.5s
[CV 3/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.544) total time=   0.3s
[CV 7/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.6s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.3s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.3s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.3s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.3s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.3s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.3s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.3s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.5s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.529) total time=   0.3s
[CV 10/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.3s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.3s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.3s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.3s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.3s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.3s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.3s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.3s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time=   0.3s
[CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.5s
[CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.537) total time=   0.5s
[CV 3/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.544) total time=   0.5s
[CV 7/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.529) total time=   0.5s
[CV 10/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time=   0.5s
[CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time=   0.5s
[CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time=   0.5s
[CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time=   0.6s
[CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time=   0.5s
[CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time=   0.6s
[CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time=   0.5s
[CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time=   0.5s
[CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time=   0.5s
[CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time=   0.5s
[CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time=   0.5s
[CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time=   0.4s
[CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.529) total time=   0.6s
[CV 10/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time=   0.3s
[CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time=   0.3s
[CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.5s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.5s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.5s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.544) total time=   0.3s
[CV 7/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 10/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.544) total time=   0.4s
[CV 7/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 10/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time=   0.3s
[CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time=   0.5s
[CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time=   0.3s
[CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.558) total time=   0.3s
[CV 9/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 10/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.530) total time=   0.5s
[CV 1/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.537) total time=   0.3s
[CV 3/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.558) total time=   0.3s
[CV 9/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.558) total time=   0.3s
[CV 9/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 10/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.537) total time=   0.3s
[CV 3/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 4/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.554) total time=   0.3s
[CV 6/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 10/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.554) total time=   0.3s
[CV 6/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.546) total time=   0.3s
[CV 7/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 10/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.558) total time=   0.3s
[CV 9/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.546) total time=   0.3s
[CV 7/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.546) total time=   0.3s
[CV 7/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.558) total time=   0.3s
[CV 9/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 10/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.537) total time=   0.3s
[CV 3/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 10/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.558) total time=   0.3s
[CV 9/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.519) total time=   0.3s
[CV 8/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.558) total time=   0.3s
[CV 9/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 10/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.554) total time=   0.3s
[CV 6/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.536) total time=   0.3s
[CV 3/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 10/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 2/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 4/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.558) total time=   0.3s
[CV 9/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 10/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 10/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 10/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 2/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.555555555555557e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.555555555555557e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=5.555555555555557e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.555555555555557e-05;, score=(train=0.999, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.581395348837214e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.581395348837214e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.558) total time=   0.3s
[CV 9/10] END ccp_alpha=5.581395348837214e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.581395348837214e-05;, score=(train=0.999, test=0.529) total time=   0.4s
[CV 1/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=0.999, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=0.999, test=0.529) total time=   0.4s
[CV 1/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.524) total time=   0.3s
[CV 2/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.535) total time=   0.3s
[CV 3/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.560) total time=   0.4s
[CV 9/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.529) total time=   0.4s
[CV 1/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.524) total time=   0.3s
[CV 2/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.560) total time=   0.4s
[CV 9/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.532) total time=   0.5s
[CV 10/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.528) total time=   0.5s
[CV 1/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.559) total time=   0.3s
[CV 9/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.528) total time=   0.5s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.5s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.5s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.3s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.3s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.3s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.3s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.5s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.3s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.5s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.3s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.5s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.3s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.4s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.3s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.3s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time=   0.5s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time=   0.3s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time=   0.3s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.527) total time=   0.4s
[CV 4/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.998, test=0.535) total time=   0.4s
[CV 5/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.998, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.561) total time=   0.3s
[CV 9/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.998, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.999, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 4/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.535) total time=   0.3s
[CV 5/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.550) total time=   0.4s
[CV 7/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.999, test=0.561) total time=   0.3s
[CV 9/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.537) total time=   0.3s
[CV 10/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.999, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.536) total time=   0.3s
[CV 3/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.999, test=0.528) total time=   0.4s
[CV 4/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.535) total time=   0.4s
[CV 5/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.551) total time=   0.3s
[CV 6/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.550) total time=   0.4s
[CV 7/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.999, test=0.561) total time=   0.4s
[CV 9/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.537) total time=   0.3s
[CV 10/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.532) total time=   0.4s
[CV 3/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.526) total time=   0.4s
[CV 4/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.537) total time=   0.5s
[CV 5/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.997, test=0.549) total time=   0.4s
[CV 6/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.553) total time=   0.4s
[CV 7/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.561) total time=   0.4s
[CV 9/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.997, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.532) total time=   0.3s
[CV 3/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.526) total time=   0.4s
[CV 4/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.537) total time=   0.3s
[CV 5/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.997, test=0.549) total time=   0.4s
[CV 6/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.553) total time=   0.3s
[CV 7/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.561) total time=   0.4s
[CV 9/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.997, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.532) total time=   0.5s
[CV 1/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.998, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.533) total time=   0.4s
[CV 3/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.998, test=0.527) total time=   0.4s
[CV 4/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.537) total time=   0.4s
[CV 5/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.549) total time=   0.4s
[CV 6/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.998, test=0.562) total time=   0.3s
[CV 9/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.542) total time=   0.3s
[CV 10/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.998, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.533) total time=   0.4s
[CV 3/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.528) total time=   0.4s
[CV 4/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.536) total time=   0.4s
[CV 5/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.549) total time=   0.3s
[CV 6/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.554) total time=   0.3s
[CV 7/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.525) total time=   0.4s
[CV 8/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.560) total time=   0.4s
[CV 9/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.540) total time=   0.4s
[CV 10/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.533) total time=   0.4s
[CV 3/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.528) total time=   0.4s
[CV 4/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.536) total time=   0.4s
[CV 5/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.549) total time=   0.4s
[CV 6/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.525) total time=   0.4s
[CV 8/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.560) total time=   0.4s
[CV 9/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.540) total time=   0.4s
[CV 10/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.533) total time=   0.3s
[CV 3/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.528) total time=   0.3s
[CV 4/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.536) total time=   0.3s
[CV 5/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.549) total time=   0.4s
[CV 6/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.554) total time=   0.3s
[CV 7/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.525) total time=   0.4s
[CV 8/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.560) total time=   0.4s
[CV 9/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.540) total time=   0.4s
[CV 10/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.529) total time=   0.4s
[CV 3/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.528) total time=   0.5s
[CV 4/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.535) total time=   0.4s
[CV 5/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.548) total time=   0.5s
[CV 6/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.560) total time=   0.4s
[CV 9/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.541) total time=   0.3s
[CV 10/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.529) total time=   0.4s
[CV 1/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.529) total time=   0.4s
[CV 3/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.528) total time=   0.4s
[CV 4/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.535) total time=   0.4s
[CV 5/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.548) total time=   0.3s
[CV 6/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.560) total time=   0.4s
[CV 9/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.541) total time=   0.4s
[CV 10/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.529) total time=   0.5s
[CV 1/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.997, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.529) total time=   0.4s
[CV 3/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.997, test=0.527) total time=   0.5s
[CV 4/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.535) total time=   0.5s
[CV 5/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.548) total time=   0.4s
[CV 6/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.997, test=0.560) total time=   0.3s
[CV 9/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.541) total time=   0.4s
[CV 10/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.529) total time=   0.4s
[CV 1/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.996, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.996, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.544) total time=   0.4s
[CV 6/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.553) total time=   0.4s
[CV 7/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.996, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.996, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.544) total time=   0.6s
[CV 6/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.553) total time=   0.4s
[CV 7/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.556) total time=   0.5s
[CV 9/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.544) total time=   0.4s
[CV 6/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.553) total time=   0.4s
[CV 7/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.544) total time=   0.4s
[CV 6/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.553) total time=   0.3s
[CV 7/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.556) total time=   0.5s
[CV 9/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.544) total time=   0.4s
[CV 6/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.553) total time=   0.4s
[CV 7/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.556) total time=   0.3s
[CV 9/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.520) total time=   0.4s
[CV 2/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.534) total time=   0.5s
[CV 5/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.994, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.552) total time=   0.4s
[CV 7/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 8/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.553) total time=   0.3s
[CV 9/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.535) total time=   0.4s
[CV 10/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.520) total time=   0.4s
[CV 2/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.525) total time=   0.3s
[CV 3/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.994, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.551) total time=   0.4s
[CV 7/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.994, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.553) total time=   0.4s
[CV 9/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.994, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.520) total time=   0.4s
[CV 2/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.994, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.550) total time=   0.4s
[CV 7/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.994, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.994, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.518) total time=   0.4s
[CV 2/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.994, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.550) total time=   0.4s
[CV 7/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.994, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.550) total time=   0.4s
[CV 9/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.994, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.518) total time=   0.4s
[CV 2/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.550) total time=   0.4s
[CV 7/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.550) total time=   0.6s
[CV 9/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.518) total time=   0.3s
[CV 2/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.550) total time=   0.4s
[CV 7/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.550) total time=   0.4s
[CV 9/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.532) total time=   0.3s
[CV 10/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.518) total time=   0.4s
[CV 2/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.550) total time=   0.4s
[CV 7/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.549) total time=   0.3s
[CV 9/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.518) total time=   0.4s
[CV 2/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.524) total time=   0.3s
[CV 3/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.550) total time=   0.5s
[CV 7/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.995, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.995, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.994, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.994, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.995, test=0.550) total time=   0.4s
[CV 7/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.994, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.995, test=0.532) total time=   0.4s
[CV 10/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.994, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.995, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.995, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.994, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.994, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.995, test=0.550) total time=   0.4s
[CV 7/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.994, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.995, test=0.532) total time=   0.3s
[CV 10/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.994, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.995, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.995, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.994, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.994, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.995, test=0.550) total time=   0.5s
[CV 7/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.994, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.995, test=0.532) total time=   0.3s
[CV 10/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.994, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.517) total time=   0.3s
[CV 2/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.549) total time=   0.3s
[CV 9/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.533) total time=   0.3s
[CV 10/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.524) total time=   0.3s
[CV 3/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.529) total time=   0.3s
[CV 5/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.4e-05;, score=(train=0.995, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.4e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.4e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.4e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.4e-05;, score=(train=0.993, test=0.540) total time=   0.3s
[CV 6/10] END ccp_alpha=6.4e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.4e-05;, score=(train=0.993, test=0.512) total time=   0.3s
[CV 8/10] END ccp_alpha=6.4e-05;, score=(train=0.995, test=0.549) total time=   0.3s
[CV 9/10] END ccp_alpha=6.4e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.4e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.993, test=0.540) total time=   0.3s
[CV 6/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.993, test=0.540) total time=   0.3s
[CV 6/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.549) total time=   0.3s
[CV 9/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.534) total time=   0.3s
[CV 10/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.517) total time=   0.3s
[CV 2/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.993, test=0.540) total time=   0.3s
[CV 6/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.995, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.993, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.516) total time=   0.5s
[CV 2/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.533) total time=   0.3s
[CV 10/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.533) total time=   0.3s
[CV 10/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.516) total time=   0.3s
[CV 2/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.522) total time=   0.4s
[CV 3/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.993, test=0.539) total time=   0.3s
[CV 6/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.533) total time=   0.3s
[CV 10/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.3s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.3s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.3s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.3s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.3s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.3s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.3s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.3s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.6s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.6s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time=   0.5s
[CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.520) total time=   0.4s
[CV 4/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.516) total time=   0.5s
[CV 2/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.993, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.995, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.516) total time=   0.5s
[CV 2/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.993, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.993, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.995, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.993, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.516) total time=   0.5s
[CV 2/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.993, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.993, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 9/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.516) total time=   0.5s
[CV 2/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.516) total time=   0.5s
[CV 2/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.993, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.995, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 9/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.993, test=0.512) total time=   0.6s
[CV 8/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.993, test=0.537) total time=   0.6s
[CV 6/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.993, test=0.512) total time=   0.6s
[CV 8/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.537) total time=   0.6s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.517) total time=   0.6s
[CV 2/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.993, test=0.537) total time=   0.6s
[CV 6/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.993, test=0.511) total time=   0.7s
[CV 8/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.549) total time=   0.7s
[CV 9/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.536) total time=   0.4s
[CV 6/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.511) total time=   0.6s
[CV 8/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.536) total time=   0.6s
[CV 6/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time=   0.9s
[CV 7/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.511) total time=   1.1s
[CV 8/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time=   0.8s
[CV 9/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.533) total time=   0.7s
[CV 10/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.525) total time=   0.6s
[CV 1/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.536) total time=   0.5s
[CV 6/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.536) total time=   0.5s
[CV 6/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.536) total time=   0.5s
[CV 6/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.993, test=0.536) total time=   0.4s
[CV 6/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.993, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.993, test=0.536) total time=   0.5s
[CV 6/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.993, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.993, test=0.536) total time=   0.4s
[CV 6/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.993, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.523) total time=   0.7s
[CV 4/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time=   0.6s
[CV 7/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.993, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.550) total time=   0.5s
[CV 9/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.550) total time=   0.5s
[CV 9/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.550) total time=   0.5s
[CV 9/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.550) total time=   0.4s
[CV 9/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.550) total time=   0.5s
[CV 9/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.550) total time=   0.7s
[CV 9/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.533) total time=   0.6s
[CV 10/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.550) total time=   0.4s
[CV 9/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.550) total time=   0.4s
[CV 9/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.550) total time=   0.5s
[CV 9/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.550) total time=   0.5s
[CV 9/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.550) total time=   0.4s
[CV 9/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.524) total time=   0.6s
[CV 3/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.550) total time=   0.5s
[CV 9/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.550) total time=   0.4s
[CV 9/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.993, test=0.524) total time=   0.6s
[CV 1/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.6s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.6s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.6s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.6s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.6s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.6s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.6s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.7s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.6s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.7s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.6s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.6s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.6s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.6s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.6s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time=   0.6s
[CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.993, test=0.537) total time=   0.5s
[CV 6/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.524) total time=   0.6s
[CV 3/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.993, test=0.537) total time=   0.4s
[CV 6/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.993, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.533) total time=   0.4s
[CV 10/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.993, test=0.524) total time=   0.6s
[CV 1/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.993, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.993, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.993, test=0.538) total time=   0.4s
[CV 6/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.994, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.994, test=0.533) total time=   0.5s
[CV 10/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.993, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.992, test=0.538) total time=   0.4s
[CV 6/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.994, test=0.548) total time=   0.8s
[CV 9/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.994, test=0.534) total time=   0.7s
[CV 10/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.993, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.993, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.993, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.993, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.992, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.993, test=0.524) total time=   0.6s
[CV 1/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.993, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.993, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.992, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 9/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.994, test=0.524) total time=   0.6s
[CV 3/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.993, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.993, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.992, test=0.538) total time=   0.4s
[CV 6/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.992, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.993, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.993, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.992, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.994, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=7e-05;, score=(train=0.994, test=0.516) total time=   0.5s
[CV 2/10] END ccp_alpha=7e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=7e-05;, score=(train=0.993, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=7e-05;, score=(train=0.993, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7e-05;, score=(train=0.992, test=0.538) total time=   0.4s
[CV 6/10] END ccp_alpha=7e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7e-05;, score=(train=0.992, test=0.513) total time=   0.6s
[CV 8/10] END ccp_alpha=7e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7e-05;, score=(train=0.994, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7e-05;, score=(train=0.993, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.994, test=0.516) total time=   0.5s
[CV 2/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.994, test=0.524) total time=   0.6s
[CV 3/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.993, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.993, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.992, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.994, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.994, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.993, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.994, test=0.516) total time=   0.4s
[CV 2/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.994, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.993, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.993, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.992, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 9/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.994, test=0.516) total time=   0.5s
[CV 2/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.994, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.993, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.993, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.994, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.992, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.993, test=0.548) total time=   0.4s
[CV 9/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.994, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.993, test=0.522) total time=   0.4s
[CV 1/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.994, test=0.524) total time=   0.6s
[CV 3/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.993, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.993, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.994, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.992, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.993, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.994, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.993, test=0.522) total time=   0.4s
[CV 1/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.994, test=0.517) total time=   0.4s
[CV 2/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.992, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.993, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.992, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.993, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.992, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.993, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.993, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.993, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.992, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.993, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.992, test=0.513) total time=   0.7s
[CV 8/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.993, test=0.548) total time=   0.6s
[CV 9/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.993, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.994, test=0.517) total time=   0.6s
[CV 2/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.992, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.993, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.992, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.993, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.992, test=0.513) total time=   0.6s
[CV 8/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.993, test=0.549) total time=   0.6s
[CV 9/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.993, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.993, test=0.522) total time=   0.4s
[CV 1/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.994, test=0.517) total time=   0.5s
[CV 2/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.994, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.993, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.992, test=0.540) total time=   0.6s
[CV 6/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.993, test=0.549) total time=   0.6s
[CV 7/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.992, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.993, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.993, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.993, test=0.522) total time=   0.6s
[CV 1/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.994, test=0.518) total time=   0.6s
[CV 2/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.993, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.992, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.993, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.992, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.993, test=0.549) total time=   0.6s
[CV 9/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.993, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.994, test=0.518) total time=   0.4s
[CV 2/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.993, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.992, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.993, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.992, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.993, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.993, test=0.534) total time=   0.6s
[CV 10/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.993, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.994, test=0.518) total time=   0.4s
[CV 2/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.992, test=0.521) total time=   0.4s
[CV 4/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.993, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.992, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.993, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.992, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.993, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.993, test=0.534) total time=   0.6s
[CV 10/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.518) total time=   0.5s
[CV 2/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.518) total time=   0.5s
[CV 2/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.518) total time=   0.5s
[CV 2/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.518) total time=   0.5s
[CV 2/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.525) total time=   0.7s
[CV 3/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.540) total time=   0.6s
[CV 6/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.518) total time=   0.5s
[CV 2/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.549) total time=   0.4s
[CV 9/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.534) total time=   0.4s
[CV 10/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.523) total time=   0.6s
[CV 1/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.994, test=0.518) total time=   0.7s
[CV 2/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.994, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.993, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.992, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.993, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.992, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.993, test=0.549) total time=   0.5s
[CV 9/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.993, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.520) total time=   0.6s
[CV 2/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.992, test=0.520) total time=   0.6s
[CV 4/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.992, test=0.512) total time=   0.6s
[CV 8/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.520) total time=   0.6s
[CV 2/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.992, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.992, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.520) total time=   0.5s
[CV 2/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.992, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.992, test=0.538) total time=   0.5s
[CV 6/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.992, test=0.512) total time=   0.7s
[CV 8/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.534) total time=   0.5s
[CV 10/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.545) total time=   0.6s
[CV 7/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.991, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.991, test=0.511) total time=   0.5s
[CV 8/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.991, test=0.511) total time=   0.4s
[CV 8/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.535) total time=   0.4s
[CV 10/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time=   0.6s
[CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time=   0.4s
[CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time=   0.6s
[CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time=   0.4s
[CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time=   0.4s
[CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time=   0.4s
[CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time=   0.4s
[CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time=   0.4s
[CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time=   0.4s
[CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time=   0.4s
[CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time=   0.4s
[CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time=   0.4s
[CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time=   0.4s
[CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time=   0.4s
[CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time=   0.6s
[CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time=   0.7s
[CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time=   0.6s
[CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.993, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.993, test=0.522) total time=   0.6s
[CV 2/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.993, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.535) total time=   0.6s
[CV 10/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.521) total time=   0.6s
[CV 1/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.993, test=0.522) total time=   0.6s
[CV 2/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.535) total time=   0.6s
[CV 10/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.993, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.545) total time=   0.4s
[CV 9/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.993, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.993, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.992, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.991, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.992, test=0.546) total time=   0.6s
[CV 7/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.992, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.992, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.991, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 2/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.993, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.991, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.992, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.991, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 2/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.991, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.991, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.991, test=0.520) total time=   0.4s
[CV 1/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.991, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.991, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.991, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.991, test=0.512) total time=   0.4s
[CV 8/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.991, test=0.520) total time=   0.4s
[CV 1/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.512) total time=   0.5s
[CV 8/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.520) total time=   0.4s
[CV 1/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.512) total time=   0.6s
[CV 8/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.546) total time=   0.7s
[CV 9/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.991, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.990, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.990, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.991, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.991, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.990, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.990, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.991, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.992, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.992, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.991, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.992, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.990, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.992, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.990, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.991, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.991, test=0.520) total time=   0.4s
[CV 1/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.992, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.992, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.991, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.992, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.990, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.990, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.992, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.991, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.991, test=0.520) total time=   0.6s
[CV 1/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.992, test=0.526) total time=   0.6s
[CV 2/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.992, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.991, test=0.521) total time=   0.6s
[CV 4/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.992, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.990, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.992, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.990, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.991, test=0.545) total time=   0.4s
[CV 9/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.991, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.991, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.991, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.992, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.990, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.990, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.991, test=0.545) total time=   0.6s
[CV 9/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.991, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.991, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.545) total time=   0.6s
[CV 9/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.7s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   1.0s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.7s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.8s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.3s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.8s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.7s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.7s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.7s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.7s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.7s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.7s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.7s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.7s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.7s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.6s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.6s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.6s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.6s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.4s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.4s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.4s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time=   0.5s
[CV 1/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.991, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.990, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.991, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.989, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.990, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 1/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.991, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.990, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.991, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.989, test=0.539) total time=   0.5s
[CV 6/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.991, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.990, test=0.548) total time=   0.4s
[CV 9/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 1/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.990, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.990, test=0.518) total time=   0.6s
[CV 4/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.990, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.989, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.991, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.990, test=0.548) total time=   0.4s
[CV 9/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 1/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.990, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.990, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.990, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.989, test=0.540) total time=   0.6s
[CV 6/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.991, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.990, test=0.548) total time=   0.4s
[CV 9/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time=   0.6s
[CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time=   0.4s
[CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.990, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.990, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.990, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.989, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.991, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.990, test=0.537) total time=   0.6s
[CV 10/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.990, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.990, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.990, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.989, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.990, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.990, test=0.518) total time=   0.4s
[CV 4/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.990, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.989, test=0.540) total time=   0.6s
[CV 6/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.991, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.990, test=0.548) total time=   0.6s
[CV 9/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.989, test=0.513) total time=   0.4s
[CV 1/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.990, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.989, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.990, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.989, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.990, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.990, test=0.547) total time=   0.7s
[CV 9/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.989, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.990, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.991, test=0.524) total time=   0.6s
[CV 3/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.989, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.990, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.989, test=0.540) total time=   0.4s
[CV 6/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.990, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.990, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.990, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.990, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.989, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.990, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.989, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.990, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.990, test=0.547) total time=   0.6s
[CV 9/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.990, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.989, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.990, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.989, test=0.540) total time=   0.5s
[CV 6/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.990, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.990, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.990, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time=   0.5s
[CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time=   0.4s
[CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time=   0.4s
[CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time=   0.5s
[CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time=   0.4s
[CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time=   0.6s
[CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time=   0.6s
[CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time=   0.5s
[CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time=   0.4s
[CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time=   0.5s
[CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time=   0.5s
[CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time=   0.6s
[CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time=   0.4s
[CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time=   0.6s
[CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.990, test=0.524) total time=   0.4s
[CV 2/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.989, test=0.518) total time=   0.6s
[CV 4/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.990, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.988, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.990, test=0.549) total time=   0.6s
[CV 7/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.989, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.990, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.988, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.990, test=0.524) total time=   0.5s
[CV 2/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.991, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.989, test=0.519) total time=   0.6s
[CV 4/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.990, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.988, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.990, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.989, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.990, test=0.537) total time=   0.6s
[CV 10/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.988, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.990, test=0.524) total time=   0.5s
[CV 2/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.990, test=0.524) total time=   0.4s
[CV 3/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.989, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.990, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.988, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.990, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.989, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.988, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.990, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.990, test=0.524) total time=   0.6s
[CV 3/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.989, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.990, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.988, test=0.542) total time=   0.6s
[CV 6/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.989, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.988, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.990, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.990, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.989, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.990, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.988, test=0.542) total time=   0.6s
[CV 6/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.989, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.988, test=0.513) total time=   0.4s
[CV 1/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.990, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.990, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.989, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.990, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.988, test=0.542) total time=   0.5s
[CV 6/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.988, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.989, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.988, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.990, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.990, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.989, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.990, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.988, test=0.542) total time=   0.5s
[CV 6/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.990, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.988, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.990, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.988, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.989, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.990, test=0.524) total time=   0.5s
[CV 3/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.989, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.990, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.988, test=0.542) total time=   0.5s
[CV 6/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.988, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.990, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.988, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.989, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.990, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.989, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.990, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.988, test=0.542) total time=   0.5s
[CV 6/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.988, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.989, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.988, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.989, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.990, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.989, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.989, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.988, test=0.542) total time=   0.5s
[CV 6/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.988, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.989, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.988, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.989, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.990, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.989, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.989, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.988, test=0.542) total time=   0.5s
[CV 6/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.988, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.989, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.989, test=0.535) total time=   0.6s
[CV 10/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.988, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.989, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.990, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.989, test=0.519) total time=   0.5s
[CV 4/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.989, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.988, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.990, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.988, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.989, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.989, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.988, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.989, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.990, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.989, test=0.520) total time=   0.5s
[CV 4/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.988, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.990, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.988, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.988, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.989, test=0.538) total time=   0.6s
[CV 10/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.987, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.989, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.990, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.989, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.987, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.989, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.988, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.988, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.989, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.987, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.989, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.990, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.989, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.987, test=0.543) total time=   0.6s
[CV 6/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.989, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.988, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.988, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.989, test=0.538) total time=   0.5s
[CV 10/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.987, test=0.512) total time=   0.4s
[CV 1/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.988, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.990, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.988, test=0.518) total time=   0.6s
[CV 4/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.987, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.989, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.988, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.988, test=0.545) total time=   0.4s
[CV 9/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.989, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.987, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.988, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.990, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.988, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.989, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.987, test=0.543) total time=   0.4s
[CV 6/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.989, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.988, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.988, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.989, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.987, test=0.512) total time=   0.6s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.988, test=0.521) total time=   0.5s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.990, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.988, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.987, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.989, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.988, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.988, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.989, test=0.538) total time=   0.6s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.987, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.521) total time=   0.4s
[CV 2/10] END ccp_alpha=8e-05;, score=(train=0.990, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8e-05;, score=(train=0.987, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.538) total time=   0.5s
[CV 10/10] END ccp_alpha=8e-05;, score=(train=0.987, test=0.512) total time=   0.4s
[CV 1/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.521) total time=   0.5s
[CV 2/10] END ccp_alpha=8e-05;, score=(train=0.990, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8e-05;, score=(train=0.987, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.538) total time=   0.4s
[CV 10/10] END ccp_alpha=8e-05;, score=(train=0.987, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.522) total time=   0.6s
[CV 2/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.541) total time=   0.6s
[CV 6/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.538) total time=   0.6s
[CV 10/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.511) total time=   0.4s
[CV 1/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.545) total time=   0.4s
[CV 9/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.538) total time=   0.6s
[CV 10/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.511) total time=   0.5s
[CV 1/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.989, test=0.526) total time=   0.6s
[CV 3/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.988, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.987, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.988, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.988, test=0.538) total time=   0.5s
[CV 10/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.987, test=0.511) total time=   0.5s
[CV 1/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.989, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.987, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.988, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.988, test=0.538) total time=   0.5s
[CV 10/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.987, test=0.511) total time=   0.5s
[CV 1/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.989, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.989, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.989, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.987, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.988, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.988, test=0.538) total time=   0.5s
[CV 10/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.987, test=0.511) total time=   0.4s
[CV 1/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.989, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.989, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.987, test=0.541) total time=   0.6s
[CV 6/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.989, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.987, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.988, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.988, test=0.538) total time=   0.5s
[CV 10/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.987, test=0.511) total time=   0.4s
[CV 1/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.989, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.989, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.987, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.987, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.988, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.987, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.541) total time=   0.6s
[CV 6/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.546) total time=   0.4s
[CV 9/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.537) total time=   0.6s
[CV 10/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.525) total time=   0.6s
[CV 3/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.537) total time=   0.6s
[CV 10/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.512) total time=   0.5s
[CV 1/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.989, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.989, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.987, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.987, test=0.546) total time=   0.5s
[CV 9/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.988, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.987, test=0.512) total time=   0.6s
[CV 1/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.989, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.988, test=0.518) total time=   0.6s
[CV 4/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.989, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.987, test=0.541) total time=   0.4s
[CV 6/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.987, test=0.545) total time=   0.4s
[CV 9/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.988, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.986, test=0.512) total time=   0.6s
[CV 1/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.989, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.988, test=0.518) total time=   0.6s
[CV 4/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.989, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.986, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.987, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.988, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.986, test=0.512) total time=   0.6s
[CV 1/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.989, test=0.525) total time=   0.4s
[CV 3/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.988, test=0.517) total time=   0.6s
[CV 4/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.988, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.987, test=0.541) total time=   0.4s
[CV 6/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.987, test=0.545) total time=   0.5s
[CV 9/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.988, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.986, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.989, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.989, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.989, test=0.525) total time=   0.5s
[CV 3/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.517) total time=   0.4s
[CV 4/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.988, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.988, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.988, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.988, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.986, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.987, test=0.548) total time=   0.6s
[CV 9/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.988, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.986, test=0.513) total time=   0.4s
[CV 1/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.986, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.522) total time=   0.6s
[CV 2/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.541) total time=   0.6s
[CV 6/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.986, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.548) total time=   0.6s
[CV 9/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.987, test=0.522) total time=   0.6s
[CV 2/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.988, test=0.526) total time=   0.6s
[CV 3/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.988, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.987, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.987, test=0.548) total time=   0.6s
[CV 9/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.987, test=0.535) total time=   0.5s
[CV 10/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.986, test=0.513) total time=   0.4s
[CV 1/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.987, test=0.521) total time=   0.5s
[CV 2/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.988, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.988, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.986, test=0.541) total time=   0.6s
[CV 6/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.987, test=0.548) total time=   0.6s
[CV 9/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.987, test=0.535) total time=   0.6s
[CV 10/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.988, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.541) total time=   0.6s
[CV 6/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.988, test=0.526) total time=   0.6s
[CV 3/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.548) total time=   0.4s
[CV 9/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.987, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.988, test=0.526) total time=   0.5s
[CV 3/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.986, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.986, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.987, test=0.548) total time=   0.4s
[CV 9/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.987, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.987, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.988, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.987, test=0.517) total time=   0.6s
[CV 4/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.986, test=0.541) total time=   0.4s
[CV 6/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.987, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.987, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.987, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.988, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.986, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.987, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.987, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.986, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.987, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.988, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.987, test=0.517) total time=   0.6s
[CV 4/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.986, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.987, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 1/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.987, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.988, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.986, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.987, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.987, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.986, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.987, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.988, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.987, test=0.528) total time=   0.6s
[CV 5/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.986, test=0.541) total time=   0.5s
[CV 6/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.988, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.987, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.987, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.986, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.988, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.986, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.988, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.987, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.986, test=0.513) total time=   0.6s
[CV 1/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.988, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 1/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.988, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.517) total time=   0.4s
[CV 4/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 1/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.988, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.547) total time=   0.6s
[CV 9/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time=   0.4s
[CV 1/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.988, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 1/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.988, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.536) total time=   0.6s
[CV 10/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 1/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.988, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.543) total time=   0.4s
[CV 6/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 1/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.988, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.517) total time=   0.6s
[CV 4/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 1/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.988, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.517) total time=   0.6s
[CV 4/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.986, test=0.544) total time=   0.5s
[CV 6/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.986, test=0.514) total time=   0.6s
[CV 1/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.517) total time=   0.6s
[CV 4/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.986, test=0.544) total time=   0.5s
[CV 6/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.986, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.985, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.987, test=0.528) total time=   0.6s
[CV 5/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.986, test=0.544) total time=   0.5s
[CV 6/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.987, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.986, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.987, test=0.536) total time=   0.4s
[CV 10/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.985, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time=   0.6s
[CV 5/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.543) total time=   0.4s
[CV 6/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.985, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.548) total time=   0.5s
[CV 9/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.985, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.518) total time=   0.5s
[CV 4/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.548) total time=   0.6s
[CV 9/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.536) total time=   0.5s
[CV 10/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.985, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.987, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.986, test=0.543) total time=   0.6s
[CV 6/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.986, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.986, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.986, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.985, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.987, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.987, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.986, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.986, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.985, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.987, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.985, test=0.517) total time=   0.6s
[CV 1/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.517) total time=   0.6s
[CV 4/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.517) total time=   0.6s
[CV 1/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.543) total time=   0.4s
[CV 6/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time=   0.6s
[CV 5/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.517) total time=   0.4s
[CV 1/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.986, test=0.517) total time=   0.6s
[CV 4/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.987, test=0.528) total time=   0.6s
[CV 5/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.985, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.986, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.985, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time=   0.6s
[CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time=   0.6s
[CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time=   0.6s
[CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time=   0.4s
[CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time=   0.6s
[CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time=   0.6s
[CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time=   0.4s
[CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time=   0.4s
[CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time=   0.6s
[CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.6s
[CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time=   0.6s
[CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time=   0.5s
[CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.987, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.517) total time=   0.6s
[CV 4/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.987, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.985, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.538) total time=   0.5s
[CV 10/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.984, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.987, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.517) total time=   0.5s
[CV 4/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.543) total time=   0.5s
[CV 6/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.987, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.985, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.547) total time=   0.5s
[CV 9/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.538) total time=   0.6s
[CV 10/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.984, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.982, test=0.521) total time=   0.5s
[CV 4/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.982, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.982, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.981, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.981, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.980, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.982, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.982, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.980, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.982, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.982, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.980, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.984, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.982, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.982, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.981, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.980, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.982, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.982, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.981, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.980, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.984, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.982, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.982, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.981, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.980, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.981, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.982, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.982, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.981, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.980, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.982, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.980, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.980, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.980, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.979, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.546) total time=   0.6s
[CV 7/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.980, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.541) total time=   0.4s
[CV 10/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.980, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.541) total time=   0.5s
[CV 10/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.980, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.981, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.981, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.981, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.546) total time=   0.6s
[CV 7/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.984, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.6s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.4s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time=   0.5s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.981, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.983, test=0.529) total time=   0.5s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.981, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.980, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.980, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.980, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.979, test=0.517) total time=   0.4s
[CV 1/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.981, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.983, test=0.529) total time=   0.6s
[CV 3/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.980, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.980, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.980, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.979, test=0.517) total time=   0.6s
[CV 1/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.981, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.983, test=0.529) total time=   0.4s
[CV 3/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.980, test=0.532) total time=   0.6s
[CV 5/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.980, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.979, test=0.517) total time=   0.4s
[CV 1/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.981, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.983, test=0.529) total time=   0.5s
[CV 3/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.981, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.980, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.981, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.980, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.979, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.981, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.983, test=0.529) total time=   0.5s
[CV 3/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.981, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.980, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.980, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.980, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.980, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.979, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.981, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.983, test=0.529) total time=   0.5s
[CV 3/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.981, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.980, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.980, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.979, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.980, test=0.543) total time=   0.6s
[CV 10/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.979, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.981, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.983, test=0.529) total time=   0.5s
[CV 3/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.981, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.980, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.979, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.980, test=0.543) total time=   0.6s
[CV 10/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.979, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.983, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.981, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.543) total time=   0.4s
[CV 10/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.981, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.978, test=0.517) total time=   0.6s
[CV 1/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.983, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.981, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.533) total time=   0.6s
[CV 5/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.543) total time=   0.4s
[CV 10/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.981, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.983, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.979, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.979, test=0.551) total time=   0.6s
[CV 9/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.980, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.979, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.980, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.980, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.979, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.980, test=0.533) total time=   0.6s
[CV 5/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.980, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.982, test=0.527) total time=   0.6s
[CV 3/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.980, test=0.533) total time=   0.6s
[CV 5/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.978, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.980, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.980, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.980, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.980, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.979, test=0.546) total time=   0.4s
[CV 10/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.980, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.980, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.979, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.6s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.4s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.980, test=0.529) total time=   0.5s
[CV 2/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.980, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.979, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.978, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.980, test=0.529) total time=   0.5s
[CV 2/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.979, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.979, test=0.551) total time=   0.6s
[CV 6/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.979, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.978, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.978, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.980, test=0.529) total time=   0.5s
[CV 2/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.980, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.979, test=0.551) total time=   0.4s
[CV 6/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.980, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.978, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.978, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.980, test=0.529) total time=   0.6s
[CV 2/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.980, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.979, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.978, test=0.551) total time=   0.4s
[CV 9/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.979, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.978, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.980, test=0.529) total time=   0.5s
[CV 2/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.980, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.978, test=0.551) total time=   0.5s
[CV 9/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.979, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.978, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.979, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.982, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.980, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.980, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.978, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.978, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.982, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.979, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.979, test=0.533) total time=   0.6s
[CV 5/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.980, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.977, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.978, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.982, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.979, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.979, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.979, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.979, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.980, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.978, test=0.544) total time=   0.4s
[CV 10/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.978, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.982, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.982, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.982, test=0.528) total time=   0.4s
[CV 3/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.533) total time=   0.6s
[CV 5/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.544) total time=   0.4s
[CV 10/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.982, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.979, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.979, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.979, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.979, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.978, test=0.520) total time=   0.6s
[CV 1/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.982, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.526) total time=   0.4s
[CV 4/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.977, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.544) total time=   0.6s
[CV 10/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.982, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.544) total time=   0.4s
[CV 10/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.982, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.979, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.979, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.979, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.977, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.978, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.978, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.982, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.979, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.978, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.979, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.978, test=0.544) total time=   0.4s
[CV 10/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.978, test=0.520) total time=   0.6s
[CV 1/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.982, test=0.528) total time=   0.5s
[CV 3/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.979, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.979, test=0.533) total time=   0.6s
[CV 5/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.978, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.979, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.979, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.977, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.978, test=0.544) total time=   0.6s
[CV 10/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.978, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.982, test=0.528) total time=   0.6s
[CV 3/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.979, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.979, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.978, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.979, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.979, test=0.519) total time=   0.6s
[CV 8/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.978, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.979, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.978, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.977, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.977, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.978, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.982, test=0.527) total time=   0.6s
[CV 3/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.979, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.979, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.978, test=0.544) total time=   0.4s
[CV 10/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.977, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.978, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.979, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.977, test=0.520) total time=   0.5s
[CV 1/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.979, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.979, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.977, test=0.520) total time=   0.4s
[CV 1/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.978, test=0.526) total time=   0.6s
[CV 2/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.979, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.979, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.978, test=0.544) total time=   0.6s
[CV 10/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.977, test=0.520) total time=   0.4s
[CV 1/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.978, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.979, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.979, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.978, test=0.544) total time=   0.4s
[CV 10/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.978, test=0.526) total time=   0.6s
[CV 2/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.979, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.978, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.979, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.979, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.977, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.978, test=0.544) total time=   0.6s
[CV 10/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time=   0.6s
[CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time=   0.6s
[CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time=   0.4s
[CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time=   0.6s
[CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time=   0.5s
[CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.978, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.982, test=0.527) total time=   0.6s
[CV 3/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.979, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.978, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.978, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.979, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.979, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.978, test=0.544) total time=   0.4s
[CV 10/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.977, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.978, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.982, test=0.527) total time=   0.4s
[CV 3/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.979, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.978, test=0.532) total time=   0.6s
[CV 5/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.979, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.979, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.977, test=0.552) total time=   0.6s
[CV 9/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.978, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.977, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.978, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.981, test=0.527) total time=   0.6s
[CV 3/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.978, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.978, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.978, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.978, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.979, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.977, test=0.543) total time=   0.4s
[CV 10/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.979, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.981, test=0.527) total time=   0.5s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.532) total time=   0.5s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.979, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.977, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.981, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.978, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.978, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.978, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 9/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.976, test=0.543) total time=   0.6s
[CV 10/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.977, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.977, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.981, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.978, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.978, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.978, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.979, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.977, test=0.552) total time=   0.4s
[CV 9/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.976, test=0.543) total time=   0.4s
[CV 10/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.977, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.977, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.981, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.978, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.977, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.978, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.978, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.976, test=0.553) total time=   0.4s
[CV 9/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.976, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.977, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.981, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.978, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.977, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.977, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.978, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.976, test=0.553) total time=   0.6s
[CV 9/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.976, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.981, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.978, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.978, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.981, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.978, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.978, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.553) total time=   0.6s
[CV 9/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.977, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.981, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.978, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.977, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.978, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.979, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.976, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.976, test=0.543) total time=   0.4s
[CV 10/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.976, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.977, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.981, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.978, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.977, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.978, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.978, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.976, test=0.553) total time=   0.6s
[CV 9/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.976, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.977, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.981, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.978, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.977, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.977, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.978, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.978, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.976, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.976, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.977, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.981, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.978, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.977, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.977, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.977, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.978, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.976, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.976, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.977, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.980, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.977, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.977, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.977, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.978, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.975, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.976, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.977, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.980, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.977, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.977, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.977, test=0.547) total time=   0.6s
[CV 7/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.975, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.976, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.977, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.980, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.977, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.976, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.977, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.978, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.975, test=0.554) total time=   0.6s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.976, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.977, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.980, test=0.531) total time=   0.6s
[CV 3/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.977, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.976, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.977, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.977, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.978, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.975, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.976, test=0.518) total time=   0.6s
[CV 1/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.977, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.980, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.977, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.977, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.978, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.976, test=0.518) total time=   0.6s
[CV 1/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.977, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.980, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.977, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.977, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.977, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.978, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.976, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.976, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.977, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.980, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.977, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.976, test=0.551) total time=   0.5s
[CV 6/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.977, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.978, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.975, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.976, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.976, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.980, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.977, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.976, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.977, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.975, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.975, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.975, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.976, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.980, test=0.531) total time=   0.4s
[CV 3/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.977, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.976, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.977, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.975, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.975, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.976, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.980, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.977, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.976, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.976, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.977, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.975, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.975, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.980, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.980, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.527) total time=   0.4s
[CV 2/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.980, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.976, test=0.526) total time=   0.6s
[CV 2/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.980, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.977, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.976, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.976, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.976, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.977, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.975, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.975, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.980, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.542) total time=   0.4s
[CV 10/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.980, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.980, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.542) total time=   0.6s
[CV 10/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.526) total time=   0.4s
[CV 2/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.980, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.976, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.980, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.977, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.976, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.976, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.976, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.977, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.975, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.975, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.976, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.980, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.976, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.976, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.976, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.976, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.977, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.975, test=0.554) total time=   0.6s
[CV 9/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.975, test=0.542) total time=   0.5s
[CV 10/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.976, test=0.527) total time=   0.5s
[CV 2/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.980, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.976, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.976, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.976, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.977, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.974, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.975, test=0.543) total time=   0.5s
[CV 10/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.976, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.979, test=0.532) total time=   0.4s
[CV 3/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.976, test=0.524) total time=   0.4s
[CV 4/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.975, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.976, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.977, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.974, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.974, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.974, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.976, test=0.526) total time=   0.5s
[CV 2/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 3/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.975, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.975, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.976, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.976, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.974, test=0.554) total time=   0.6s
[CV 9/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.974, test=0.544) total time=   0.4s
[CV 10/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.974, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.979, test=0.532) total time=   0.5s
[CV 3/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.975, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.975, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.976, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.976, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.974, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.974, test=0.544) total time=   0.5s
[CV 10/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.974, test=0.520) total time=   0.4s
[CV 1/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.975, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.975, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.976, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.973, test=0.553) total time=   0.4s
[CV 9/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.975, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.975, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.976, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.973, test=0.553) total time=   0.6s
[CV 9/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.978, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.976, test=0.520) total time=   0.6s
[CV 8/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.976, test=0.520) total time=   0.6s
[CV 8/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.974, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.973, test=0.554) total time=   0.6s
[CV 9/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.978, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.6s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.6s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.6s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.6s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.6s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.6s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.6s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.6s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.6s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.6s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.6s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.4s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.7s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.6s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.6s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.4s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.4s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.6s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.4s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.6s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.4s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.4s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.4s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.6s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.6s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.6s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.6s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time=   0.6s
[CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time=   0.5s
[CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.974, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.973, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.972, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.978, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.553) total time=   0.6s
[CV 6/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.976, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.972, test=0.555) total time=   0.4s
[CV 9/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.973, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.978, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.976, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.972, test=0.555) total time=   0.6s
[CV 9/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.973, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.975, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.975, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.975, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.972, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.975, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.975, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.972, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.973, test=0.546) total time=   0.4s
[CV 10/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time=   0.4s
[CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time=   0.4s
[CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time=   0.4s
[CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time=   0.6s
[CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time=   0.6s
[CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time=   0.6s
[CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time=   0.4s
[CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time=   0.6s
[CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.975, test=0.545) total time=   0.6s
[CV 7/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.975, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.972, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.973, test=0.517) total time=   0.6s
[CV 1/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.975, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.972, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.978, test=0.531) total time=   0.4s
[CV 3/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.974, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.975, test=0.545) total time=   0.5s
[CV 7/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.975, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.972, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.973, test=0.546) total time=   0.4s
[CV 10/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.973, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.975, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.974, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.975, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.972, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.975, test=0.525) total time=   0.6s
[CV 2/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.973, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.975, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.974, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.975, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.972, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.973, test=0.517) total time=   0.6s
[CV 1/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.978, test=0.531) total time=   0.4s
[CV 3/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.972, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.546) total time=   0.6s
[CV 10/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.517) total time=   0.4s
[CV 1/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.978, test=0.531) total time=   0.4s
[CV 3/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.972, test=0.553) total time=   0.6s
[CV 9/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.517) total time=   0.4s
[CV 1/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.525) total time=   0.4s
[CV 2/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.972, test=0.553) total time=   0.4s
[CV 9/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.517) total time=   0.4s
[CV 1/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.525) total time=   0.5s
[CV 2/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.978, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.972, test=0.553) total time=   0.5s
[CV 9/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.517) total time=   0.6s
[CV 1/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.974, test=0.524) total time=   0.5s
[CV 2/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.977, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.973, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.974, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.974, test=0.546) total time=   0.4s
[CV 7/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.974, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.971, test=0.556) total time=   0.5s
[CV 9/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.972, test=0.546) total time=   0.5s
[CV 10/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.972, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.977, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.970, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.972, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.971, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.977, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.552) total time=   0.4s
[CV 6/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.970, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.972, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.971, test=0.518) total time=   0.4s
[CV 1/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.977, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.546) total time=   0.6s
[CV 7/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.970, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.972, test=0.547) total time=   0.4s
[CV 10/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.971, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.977, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.973, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.974, test=0.552) total time=   0.7s
[CV 6/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.974, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.973, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.970, test=0.557) total time=   0.4s
[CV 9/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.972, test=0.548) total time=   0.4s
[CV 10/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.971, test=0.518) total time=   0.6s
[CV 1/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.977, test=0.531) total time=   0.4s
[CV 3/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.974, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.973, test=0.530) total time=   0.6s
[CV 5/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.974, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.974, test=0.546) total time=   0.5s
[CV 7/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.973, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.970, test=0.557) total time=   0.5s
[CV 9/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.972, test=0.548) total time=   0.5s
[CV 10/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.971, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.522) total time=   0.4s
[CV 4/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.974, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.969, test=0.556) total time=   0.6s
[CV 9/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.974, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.969, test=0.556) total time=   0.5s
[CV 9/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.974, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.547) total time=   0.4s
[CV 7/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.518) total time=   0.4s
[CV 8/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.969, test=0.556) total time=   0.5s
[CV 9/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.523) total time=   0.6s
[CV 2/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.974, test=0.552) total time=   0.6s
[CV 6/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.969, test=0.556) total time=   0.4s
[CV 9/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.547) total time=   0.4s
[CV 10/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.973, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.972, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.974, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.972, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.969, test=0.556) total time=   0.5s
[CV 9/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.971, test=0.547) total time=   0.4s
[CV 10/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.973, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.973, test=0.522) total time=   0.6s
[CV 4/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.974, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.972, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.969, test=0.556) total time=   0.5s
[CV 9/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.971, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.973, test=0.522) total time=   0.5s
[CV 4/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.974, test=0.552) total time=   0.5s
[CV 6/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.973, test=0.547) total time=   0.5s
[CV 7/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.972, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.969, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.971, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.971, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time=   0.6s
[CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time=   0.4s
[CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time=   0.6s
[CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time=   0.5s
[CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time=   0.6s
[CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time=   0.6s
[CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time=   0.5s
[CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time=   0.4s
[CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time=   0.6s
[CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time=   0.5s
[CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time=   0.6s
[CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time=   0.6s
[CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time=   0.5s
[CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time=   0.5s
[CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time=   0.5s
[CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.972, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.976, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.973, test=0.524) total time=   0.6s
[CV 4/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.968, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.970, test=0.548) total time=   0.5s
[CV 10/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.972, test=0.522) total time=   0.6s
[CV 2/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.976, test=0.530) total time=   0.6s
[CV 3/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.968, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.970, test=0.548) total time=   0.5s
[CV 10/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.972, test=0.522) total time=   0.6s
[CV 2/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.976, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.971, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.968, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.970, test=0.548) total time=   0.6s
[CV 10/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.970, test=0.518) total time=   0.6s
[CV 1/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.972, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.976, test=0.530) total time=   0.5s
[CV 3/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.973, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.973, test=0.553) total time=   0.6s
[CV 6/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.968, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.970, test=0.548) total time=   0.5s
[CV 10/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.972, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.976, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.973, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.971, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.968, test=0.555) total time=   0.5s
[CV 9/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.970, test=0.548) total time=   0.6s
[CV 10/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.972, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.976, test=0.530) total time=   0.4s
[CV 3/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.973, test=0.524) total time=   0.5s
[CV 4/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.973, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.971, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.968, test=0.555) total time=   0.6s
[CV 9/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.970, test=0.548) total time=   0.4s
[CV 10/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.970, test=0.518) total time=   0.5s
[CV 1/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.975, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.970, test=0.549) total time=   0.5s
[CV 10/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.969, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.975, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.970, test=0.549) total time=   0.5s
[CV 10/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.969, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.522) total time=   0.6s
[CV 2/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.975, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.970, test=0.549) total time=   0.5s
[CV 10/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.969, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.975, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.553) total time=   0.4s
[CV 6/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.548) total time=   0.6s
[CV 7/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.970, test=0.549) total time=   0.6s
[CV 10/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.969, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.972, test=0.522) total time=   0.6s
[CV 2/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.975, test=0.531) total time=   0.4s
[CV 3/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.973, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.971, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.968, test=0.554) total time=   0.6s
[CV 9/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.970, test=0.549) total time=   0.5s
[CV 10/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.969, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.972, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.975, test=0.531) total time=   0.4s
[CV 3/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.971, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.973, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.971, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.968, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.970, test=0.548) total time=   0.5s
[CV 10/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.969, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.972, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.975, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.973, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.973, test=0.548) total time=   0.4s
[CV 7/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.971, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.970, test=0.548) total time=   0.5s
[CV 10/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.969, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.972, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.975, test=0.531) total time=   0.4s
[CV 3/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.972, test=0.553) total time=   0.6s
[CV 6/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.972, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.971, test=0.516) total time=   0.4s
[CV 8/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.970, test=0.548) total time=   0.5s
[CV 10/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.969, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.522) total time=   0.6s
[CV 2/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.975, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.970, test=0.548) total time=   0.5s
[CV 10/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.969, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.975, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.968, test=0.554) total time=   0.4s
[CV 9/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.970, test=0.548) total time=   0.4s
[CV 10/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.969, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.975, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.529) total time=   0.5s
[CV 5/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.970, test=0.548) total time=   0.6s
[CV 10/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.969, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.971, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.975, test=0.532) total time=   0.5s
[CV 3/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.972, test=0.523) total time=   0.5s
[CV 4/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.971, test=0.529) total time=   0.6s
[CV 5/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.972, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.972, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.970, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.969, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.969, test=0.519) total time=   0.6s
[CV 1/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.971, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.975, test=0.532) total time=   0.5s
[CV 3/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.523) total time=   0.6s
[CV 4/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.970, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.553) total time=   0.6s
[CV 6/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.970, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.969, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.969, test=0.519) total time=   0.5s
[CV 1/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.971, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.975, test=0.532) total time=   0.6s
[CV 3/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.523) total time=   0.4s
[CV 4/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.970, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.553) total time=   0.5s
[CV 6/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.970, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.969, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.969, test=0.519) total time=   0.4s
[CV 1/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.971, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.974, test=0.531) total time=   0.5s
[CV 3/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.972, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.969, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.971, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.972, test=0.548) total time=   0.5s
[CV 7/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.969, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.969, test=0.547) total time=   0.4s
[CV 10/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.969, test=0.517) total time=   0.4s
[CV 1/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.971, test=0.523) total time=   0.6s
[CV 2/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.974, test=0.531) total time=   0.4s
[CV 3/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.972, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.969, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.971, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.972, test=0.549) total time=   0.6s
[CV 7/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.969, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.969, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.969, test=0.517) total time=   0.6s
[CV 1/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.971, test=0.523) total time=   0.5s
[CV 2/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.974, test=0.532) total time=   0.5s
[CV 3/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.972, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.969, test=0.530) total time=   0.5s
[CV 5/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.971, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.972, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.969, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.968, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.969, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.969, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.971, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.974, test=0.532) total time=   0.4s
[CV 3/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.972, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.969, test=0.531) total time=   0.6s
[CV 5/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.971, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.972, test=0.549) total time=   0.6s
[CV 7/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.969, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.969, test=0.547) total time=   0.6s
[CV 10/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.969, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.971, test=0.522) total time=   0.4s
[CV 2/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.974, test=0.532) total time=   0.4s
[CV 3/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.972, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.971, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.972, test=0.549) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.517) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.971, test=0.522) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.974, test=0.532) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.972, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.531) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.971, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.972, test=0.549) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.547) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.517) total time=   0.4s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.6s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.6s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.6s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.6s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.6s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.4s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.4s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.6s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.6s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.6s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.6s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.6s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.4s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.6s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.6s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.6s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.4s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.4s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.6s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.4s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.6s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.6s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.4s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.6s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.6s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.4s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.6s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.6s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.6s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.4s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.6s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.6s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.6s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.6s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.6s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.6s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.4s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.6s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.6s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.6s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.4s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.6s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.4s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.6s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.6s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.6s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.6s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.6s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time=   0.6s
[CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time=   0.5s
[CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time=   0.6s
[CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.965, test=0.528) total time=   0.6s
[CV 2/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.967, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.963, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.966, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.967, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.962, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.962, test=0.552) total time=   0.5s
[CV 10/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.967, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.963, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.966, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.967, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.962, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.962, test=0.552) total time=   0.4s
[CV 10/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.964, test=0.521) total time=   0.6s
[CV 1/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.965, test=0.528) total time=   0.4s
[CV 2/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.967, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.963, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.966, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.967, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.964, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.962, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.962, test=0.552) total time=   0.5s
[CV 10/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.969, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.967, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.963, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.966, test=0.554) total time=   0.4s
[CV 6/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.967, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.964, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.962, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.962, test=0.552) total time=   0.5s
[CV 10/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.964, test=0.521) total time=   0.4s
[CV 1/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.965, test=0.528) total time=   0.5s
[CV 2/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.967, test=0.526) total time=   0.5s
[CV 4/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.963, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.966, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.967, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.964, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.962, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.962, test=0.552) total time=   0.5s
[CV 10/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.964, test=0.529) total time=   0.5s
[CV 2/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.969, test=0.535) total time=   0.4s
[CV 3/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.966, test=0.527) total time=   0.5s
[CV 4/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.963, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.966, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.967, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.963, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.962, test=0.559) total time=   0.6s
[CV 9/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.962, test=0.552) total time=   0.5s
[CV 10/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.964, test=0.529) total time=   0.5s
[CV 2/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.966, test=0.527) total time=   0.6s
[CV 4/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.963, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.966, test=0.555) total time=   0.4s
[CV 6/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.967, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.963, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.962, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.962, test=0.552) total time=   0.5s
[CV 10/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.964, test=0.529) total time=   0.5s
[CV 2/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.969, test=0.535) total time=   0.5s
[CV 3/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.966, test=0.527) total time=   0.6s
[CV 4/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.963, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.966, test=0.555) total time=   0.4s
[CV 6/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.967, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.963, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.962, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.962, test=0.552) total time=   0.5s
[CV 10/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.964, test=0.521) total time=   0.5s
[CV 1/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.963, test=0.532) total time=   0.5s
[CV 2/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.964, test=0.529) total time=   0.5s
[CV 4/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.962, test=0.524) total time=   0.4s
[CV 5/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.965, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.966, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.962, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.961, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.961, test=0.554) total time=   0.4s
[CV 10/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.963, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.963, test=0.532) total time=   0.6s
[CV 2/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.962, test=0.525) total time=   0.4s
[CV 5/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.965, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.965, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.962, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.960, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.961, test=0.553) total time=   0.4s
[CV 10/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.963, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.963, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.962, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.965, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.964, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.962, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.960, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.963, test=0.522) total time=   0.6s
[CV 1/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.963, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.962, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.965, test=0.555) total time=   0.6s
[CV 6/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.964, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.962, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.960, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.963, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.962, test=0.525) total time=   0.6s
[CV 5/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.964, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.964, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.962, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.960, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.962, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.960, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.531) total time=   0.6s
[CV 4/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.556) total time=   0.4s
[CV 6/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.515) total time=   0.4s
[CV 8/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.960, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.962, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.964, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.964, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.961, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.960, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.962, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time=   0.6s
[CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time=   0.6s
[CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time=   0.4s
[CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time=   0.4s
[CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time=   0.6s
[CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time=   0.6s
[CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time=   0.6s
[CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time=   0.6s
[CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.962, test=0.533) total time=   0.5s
[CV 2/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.967, test=0.536) total time=   0.4s
[CV 3/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.963, test=0.530) total time=   0.5s
[CV 4/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.961, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.963, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.963, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.960, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.959, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.960, test=0.552) total time=   0.6s
[CV 10/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.961, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.962, test=0.533) total time=   0.5s
[CV 2/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.967, test=0.536) total time=   0.5s
[CV 3/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.963, test=0.530) total time=   0.5s
[CV 4/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.961, test=0.525) total time=   0.6s
[CV 5/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.963, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.963, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.960, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.959, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.960, test=0.552) total time=   0.6s
[CV 10/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.961, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.962, test=0.533) total time=   0.5s
[CV 2/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.966, test=0.537) total time=   0.5s
[CV 3/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.530) total time=   0.5s
[CV 4/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.961, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.554) total time=   0.5s
[CV 7/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.960, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.959, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.960, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.961, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.962, test=0.533) total time=   0.5s
[CV 2/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.966, test=0.537) total time=   0.6s
[CV 3/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.530) total time=   0.6s
[CV 4/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.961, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.554) total time=   0.4s
[CV 7/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.960, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.959, test=0.559) total time=   0.6s
[CV 9/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.960, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.961, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.962, test=0.533) total time=   0.4s
[CV 2/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.966, test=0.537) total time=   0.4s
[CV 3/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.963, test=0.530) total time=   0.5s
[CV 4/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.961, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.963, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.963, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.960, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.959, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.960, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.961, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.962, test=0.533) total time=   0.5s
[CV 2/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.966, test=0.537) total time=   0.5s
[CV 3/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.963, test=0.529) total time=   0.5s
[CV 4/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.960, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.963, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.963, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.960, test=0.514) total time=   0.6s
[CV 8/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.959, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.959, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.961, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.965, test=0.538) total time=   0.6s
[CV 3/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.960, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.962, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.963, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.960, test=0.515) total time=   0.5s
[CV 8/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.959, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.959, test=0.551) total time=   0.5s
[CV 10/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.961, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.960, test=0.534) total time=   0.6s
[CV 2/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.961, test=0.558) total time=   0.4s
[CV 6/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.962, test=0.555) total time=   0.6s
[CV 7/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.959, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.6s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.4s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.6s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.4s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.4s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.6s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.6s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.4s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.6s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.6s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.4s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.6s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.6s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.4s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.6s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.6s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.4s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.6s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.4s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.4s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.6s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.6s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.6s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.4s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.6s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.6s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.6s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.4s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.4s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time=   0.6s
[CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time=   0.4s
[CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.960, test=0.534) total time=   0.6s
[CV 2/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.965, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.962, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.961, test=0.558) total time=   0.4s
[CV 6/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.962, test=0.555) total time=   0.6s
[CV 7/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.959, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.958, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.959, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.960, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.964, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.962, test=0.532) total time=   0.5s
[CV 4/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.961, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.962, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.958, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.958, test=0.558) total time=   0.4s
[CV 9/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.958, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.959, test=0.524) total time=   0.6s
[CV 1/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.959, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.964, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.962, test=0.531) total time=   0.4s
[CV 4/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.961, test=0.557) total time=   0.6s
[CV 6/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.961, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.958, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.958, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.958, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.959, test=0.524) total time=   0.6s
[CV 1/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.959, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.964, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.961, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.959, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.961, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.961, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.958, test=0.513) total time=   0.5s
[CV 8/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.957, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.958, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.959, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.959, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.964, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.961, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.959, test=0.528) total time=   0.6s
[CV 5/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.961, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.961, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.958, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.957, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.958, test=0.553) total time=   0.6s
[CV 10/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.959, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.959, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.964, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.961, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.959, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.961, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.961, test=0.555) total time=   0.5s
[CV 7/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.958, test=0.514) total time=   0.4s
[CV 8/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.957, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.958, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.959, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.959, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.964, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.961, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.959, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.961, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.961, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.958, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.957, test=0.559) total time=   0.6s
[CV 9/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.958, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.959, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.959, test=0.534) total time=   0.5s
[CV 2/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.964, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.961, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.959, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.960, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.961, test=0.555) total time=   0.4s
[CV 7/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.958, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.957, test=0.559) total time=   0.6s
[CV 9/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.957, test=0.553) total time=   0.5s
[CV 10/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.958, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.958, test=0.533) total time=   0.5s
[CV 2/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.964, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.961, test=0.532) total time=   0.6s
[CV 4/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.958, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.960, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.960, test=0.556) total time=   0.5s
[CV 7/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.957, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.956, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.956, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.958, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.958, test=0.533) total time=   0.4s
[CV 2/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.964, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.961, test=0.532) total time=   0.6s
[CV 4/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.958, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.960, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.960, test=0.556) total time=   0.5s
[CV 7/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.957, test=0.514) total time=   0.5s
[CV 8/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.956, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.956, test=0.554) total time=   0.5s
[CV 10/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.958, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.957, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.963, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.960, test=0.529) total time=   0.5s
[CV 4/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.957, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.959, test=0.558) total time=   0.4s
[CV 6/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.959, test=0.556) total time=   0.5s
[CV 7/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.956, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.955, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.955, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.957, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.957, test=0.536) total time=   0.4s
[CV 2/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.963, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.960, test=0.529) total time=   0.5s
[CV 4/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.957, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.959, test=0.558) total time=   0.4s
[CV 6/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.959, test=0.556) total time=   0.5s
[CV 7/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.956, test=0.516) total time=   0.6s
[CV 8/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.955, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.957, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.957, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.963, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.960, test=0.529) total time=   0.5s
[CV 4/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.956, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.959, test=0.558) total time=   0.4s
[CV 6/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.959, test=0.556) total time=   0.5s
[CV 7/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.956, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.955, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.957, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.957, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.963, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.960, test=0.529) total time=   0.5s
[CV 4/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.956, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.959, test=0.557) total time=   0.4s
[CV 6/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.959, test=0.556) total time=   0.5s
[CV 7/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.955, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.954, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.956, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.957, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.962, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.960, test=0.529) total time=   0.5s
[CV 4/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.956, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.959, test=0.556) total time=   0.6s
[CV 7/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.955, test=0.516) total time=   0.4s
[CV 8/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.954, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.954, test=0.556) total time=   0.4s
[CV 10/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.956, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.957, test=0.536) total time=   0.6s
[CV 2/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.962, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.960, test=0.529) total time=   0.5s
[CV 4/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.956, test=0.528) total time=   0.5s
[CV 5/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.959, test=0.556) total time=   0.6s
[CV 7/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.955, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.954, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.954, test=0.556) total time=   0.4s
[CV 10/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.956, test=0.525) total time=   0.5s
[CV 1/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.957, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.962, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.959, test=0.530) total time=   0.5s
[CV 4/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.956, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.958, test=0.556) total time=   0.6s
[CV 6/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.958, test=0.556) total time=   0.5s
[CV 7/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.955, test=0.516) total time=   0.4s
[CV 8/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.954, test=0.558) total time=   0.5s
[CV 9/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.956, test=0.522) total time=   0.6s
[CV 1/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.957, test=0.554) total time=   0.5s
[CV 6/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.958, test=0.557) total time=   0.4s
[CV 7/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.956, test=0.522) total time=   0.6s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.6s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.6s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.6s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.6s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.6s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.6s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.6s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.6s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.6s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.6s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time=   0.6s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time=   0.6s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time=   0.6s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.959, test=0.531) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.955, test=0.526) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.958, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.953, test=0.559) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.954, test=0.556) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.956, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.955, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.961, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.959, test=0.530) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.954, test=0.525) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.956, test=0.555) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.957, test=0.557) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.954, test=0.517) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.952, test=0.560) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.952, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.956, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.960, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.958, test=0.532) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.953, test=0.525) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.955, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.956, test=0.558) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.954, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.951, test=0.561) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.951, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.955, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.960, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.958, test=0.532) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.953, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.955, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.956, test=0.558) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.954, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.951, test=0.561) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.951, test=0.556) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.955, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.960, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.957, test=0.534) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.953, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.955, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.956, test=0.558) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.954, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.951, test=0.561) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.951, test=0.557) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.955, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.959, test=0.542) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.957, test=0.533) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.953, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.955, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.956, test=0.558) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.953, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.951, test=0.561) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.951, test=0.557) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.955, test=0.524) total time=   0.6s
[CV 1/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.959, test=0.542) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.957, test=0.533) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.953, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.955, test=0.556) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.956, test=0.558) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.953, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.951, test=0.561) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.951, test=0.557) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.955, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.959, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.957, test=0.533) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.953, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.955, test=0.557) total time=   0.6s
[CV 6/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.956, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.953, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.951, test=0.561) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.951, test=0.557) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.955, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.959, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.956, test=0.533) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.952, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.954, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.956, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.953, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.951, test=0.561) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.951, test=0.557) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.954, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.959, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.956, test=0.533) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.952, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.954, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.956, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.953, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.950, test=0.562) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.951, test=0.557) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.954, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.959, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.956, test=0.533) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.952, test=0.527) total time=   0.6s
[CV 5/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.954, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.955, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.953, test=0.519) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.950, test=0.562) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.954, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.959, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.956, test=0.533) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.952, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.954, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.955, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.952, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.950, test=0.562) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.954, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.954, test=0.535) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.959, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.956, test=0.533) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.952, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.954, test=0.557) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.955, test=0.560) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.952, test=0.519) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.950, test=0.561) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.954, test=0.524) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.950, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.948, test=0.526) total time=   0.6s
[CV 5/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.948, test=0.520) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.945, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.946, test=0.563) total time=   0.6s
[CV 10/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.950, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.948, test=0.520) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.945, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.946, test=0.563) total time=   0.6s
[CV 10/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time=   0.6s
[CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time=   0.6s
[CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time=   0.6s
[CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time=   0.6s
[CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time=   0.6s
[CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.6s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.6s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.6s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.6s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.6s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.6s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.6s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.6s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.948, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.950, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.955, test=0.539) total time=   0.6s
[CV 3/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.948, test=0.525) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.950, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.945, test=0.563) total time=   0.6s
[CV 10/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.949, test=0.522) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.955, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.950, test=0.560) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.951, test=0.559) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.948, test=0.521) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.949, test=0.537) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.955, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.951, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.948, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.950, test=0.560) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.951, test=0.559) total time=   0.6s
[CV 7/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.947, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.944, test=0.567) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.947, test=0.538) total time=   0.6s
[CV 2/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.954, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.950, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.947, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.949, test=0.561) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.950, test=0.561) total time=   0.6s
[CV 7/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.947, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.943, test=0.566) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.947, test=0.538) total time=   0.6s
[CV 2/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.954, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.950, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.947, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.949, test=0.561) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.950, test=0.561) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.947, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.943, test=0.566) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.949, test=0.522) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.947, test=0.538) total time=   0.6s
[CV 2/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.954, test=0.541) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.950, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.947, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.949, test=0.561) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.950, test=0.561) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.947, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.943, test=0.566) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.949, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.947, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.954, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.950, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.947, test=0.525) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.949, test=0.561) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.950, test=0.561) total time=   0.6s
[CV 7/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.947, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.943, test=0.566) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.945, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.948, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.947, test=0.538) total time=   0.6s
[CV 2/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.954, test=0.541) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.950, test=0.535) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.948, test=0.560) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.950, test=0.561) total time=   0.6s
[CV 7/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.947, test=0.523) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.942, test=0.566) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.944, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.947, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.954, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.535) total time=   0.6s
[CV 4/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.948, test=0.560) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.561) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.942, test=0.566) total time=   0.6s
[CV 9/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.944, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.538) total time=   0.6s
[CV 2/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.954, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.535) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.948, test=0.560) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.561) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.942, test=0.566) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.944, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.538) total time=   0.6s
[CV 2/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.954, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.535) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.948, test=0.560) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.561) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.942, test=0.566) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.944, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.947, test=0.538) total time=   0.6s
[CV 2/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.954, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.950, test=0.535) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.948, test=0.560) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.942, test=0.565) total time=   0.5s
[CV 9/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.944, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.947, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.947, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.954, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.950, test=0.535) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.946, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.948, test=0.560) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.942, test=0.565) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.944, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.947, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.947, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.953, test=0.541) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.950, test=0.535) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.948, test=0.560) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.942, test=0.565) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.944, test=0.563) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.947, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.947, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.953, test=0.542) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.950, test=0.535) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.948, test=0.560) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.944, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.947, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.947, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.950, test=0.535) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.948, test=0.560) total time=   0.5s
[CV 6/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.941, test=0.564) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.944, test=0.563) total time=   0.5s
[CV 10/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.947, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.947, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.953, test=0.543) total time=   0.6s
[CV 3/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.950, test=0.535) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.948, test=0.560) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.946, test=0.522) total time=   0.6s
[CV 8/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.941, test=0.564) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.944, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.947, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.947, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.949, test=0.535) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.948, test=0.560) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.944, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.947, test=0.522) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.941, test=0.564) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.946, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time=   0.6s
[CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time=   0.6s
[CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time=   0.6s
[CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time=   0.6s
[CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time=   0.6s
[CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.536) total time=   0.6s
[CV 4/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.943, test=0.562) total time=   0.6s
[CV 10/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.562) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time=   0.6s
[CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time=   0.6s
[CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time=   0.6s
[CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time=   0.6s
[CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time=   0.6s
[CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time=   0.5s
[CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time=   0.5s
[CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time=   0.5s
[CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time=   0.5s
[CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time=   0.6s
[CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time=   0.8s
[CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time=   0.6s
[CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time=   0.5s
[CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time=   0.5s
[CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.946, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.949, test=0.536) total time=   0.5s
[CV 4/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.946, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.948, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.949, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.946, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.943, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.946, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.945, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.948, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.949, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.946, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.945, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.947, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.949, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.946, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.941, test=0.563) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.946, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.949, test=0.536) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.945, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.947, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.949, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.946, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.943, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.946, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.949, test=0.536) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.945, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.947, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.949, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.946, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.953, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.945, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.947, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.949, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.946, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.943, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.946, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.946, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.953, test=0.543) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.945, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.947, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.949, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.946, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.941, test=0.564) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.943, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.946, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.946, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.952, test=0.544) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.945, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.947, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.948, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.946, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.946, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.946, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.952, test=0.544) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.945, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.947, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.948, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.946, test=0.522) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.941, test=0.564) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.943, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.946, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.946, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.952, test=0.544) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.945, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.947, test=0.559) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.948, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.946, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.941, test=0.564) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.946, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.945, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.952, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.949, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.945, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.947, test=0.559) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.948, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.945, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.940, test=0.565) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.946, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.944, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.952, test=0.543) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.948, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.945, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.946, test=0.560) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.948, test=0.562) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.945, test=0.521) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.939, test=0.565) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.943, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.945, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.943, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.950, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.948, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.943, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.945, test=0.560) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.947, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.944, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.939, test=0.566) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.942, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.945, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.943, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.950, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.948, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.943, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.945, test=0.560) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.947, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.944, test=0.522) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.939, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.942, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.945, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.943, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.950, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.948, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.942, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.945, test=0.561) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.947, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.943, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.938, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.942, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.945, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.942, test=0.542) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.949, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.947, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.942, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.944, test=0.561) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.946, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.942, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.936, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.941, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.944, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.942, test=0.542) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.949, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.946, test=0.534) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.941, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.944, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.946, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.942, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.936, test=0.566) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.941, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.944, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.948, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.946, test=0.534) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.943, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.942, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.936, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.948, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.946, test=0.534) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.943, test=0.563) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.942, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.936, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.948, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.946, test=0.534) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.943, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.942, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.936, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.948, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.946, test=0.534) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.943, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.942, test=0.523) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.936, test=0.566) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.941, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.948, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.945, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.941, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.942, test=0.564) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.944, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.942, test=0.523) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.936, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.940, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.943, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.540) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.948, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.945, test=0.532) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.942, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.943, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.936, test=0.566) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.940, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.943, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.540) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.948, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.945, test=0.532) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.942, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.943, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.936, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.940, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.943, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.941, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.948, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.945, test=0.532) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.941, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.942, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.943, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.941, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.936, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.940, test=0.561) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.943, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.941, test=0.540) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.948, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.944, test=0.533) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.941, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.942, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.943, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.941, test=0.524) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.936, test=0.566) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.939, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.943, test=0.522) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.941, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.947, test=0.541) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.944, test=0.533) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.941, test=0.527) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.942, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.943, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.941, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.935, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.939, test=0.561) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.943, test=0.522) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.941, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.947, test=0.541) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.944, test=0.532) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.940, test=0.527) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.942, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.942, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.941, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.935, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.939, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.942, test=0.522) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.940, test=0.540) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.946, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.944, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.940, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.942, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.942, test=0.562) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.940, test=0.524) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.935, test=0.566) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.939, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.942, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.940, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.946, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.944, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.940, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.942, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.942, test=0.562) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.940, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.935, test=0.566) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.939, test=0.560) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.942, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.940, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.946, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.944, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.940, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.942, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.942, test=0.562) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.940, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.935, test=0.566) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.939, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.942, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.940, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.946, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.944, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.940, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.942, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.942, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.940, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.935, test=0.566) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.939, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.942, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.940, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.946, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.944, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.939, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.941, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.942, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.940, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.935, test=0.565) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.939, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.942, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.940, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.946, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.944, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.939, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.941, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.942, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.939, test=0.525) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.935, test=0.565) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.939, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.942, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.940, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.946, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.944, test=0.531) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.939, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.941, test=0.565) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.942, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.939, test=0.525) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.935, test=0.565) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.939, test=0.560) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.942, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.946, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.943, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.941, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.942, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.935, test=0.565) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.942, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.946, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.943, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.941, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.942, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.524) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.935, test=0.565) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.942, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.939, test=0.541) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.945, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.942, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.939, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.941, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.942, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.939, test=0.525) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.935, test=0.565) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.938, test=0.558) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.941, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.935, test=0.540) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.941, test=0.541) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.939, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.934, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.936, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.938, test=0.565) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.935, test=0.528) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.931, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.933, test=0.560) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.936, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.935, test=0.540) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.941, test=0.542) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.938, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.934, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.936, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.937, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.935, test=0.529) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.931, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.933, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.936, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.941, test=0.542) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.938, test=0.533) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.934, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.528) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.931, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.932, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.941, test=0.542) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.938, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.934, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.562) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.528) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.931, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.932, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.540) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.941, test=0.542) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.938, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.934, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.562) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.528) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.931, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.932, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.941, test=0.542) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.938, test=0.533) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.934, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.528) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.931, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.932, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.934, test=0.539) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.941, test=0.542) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.938, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.934, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.935, test=0.562) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.936, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.934, test=0.528) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.931, test=0.567) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.932, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.936, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.934, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.939, test=0.544) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.937, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.933, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.935, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.934, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.933, test=0.529) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.930, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.930, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.936, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.934, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.939, test=0.544) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.937, test=0.533) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.933, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.935, test=0.562) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.934, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.933, test=0.529) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.930, test=0.567) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.930, test=0.562) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.936, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.933, test=0.537) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.938, test=0.544) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.936, test=0.535) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.932, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.934, test=0.561) total time=   0.5s
[CV 6/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.934, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.933, test=0.529) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.930, test=0.568) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.930, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.936, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.933, test=0.537) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.938, test=0.544) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.936, test=0.535) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.932, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.934, test=0.561) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.934, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.933, test=0.529) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.930, test=0.568) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.930, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.936, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.933, test=0.537) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.938, test=0.544) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.936, test=0.535) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.932, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.934, test=0.561) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.934, test=0.564) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.933, test=0.529) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.930, test=0.568) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.930, test=0.561) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.936, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.933, test=0.537) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.938, test=0.544) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.936, test=0.535) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.932, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.934, test=0.562) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.934, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.933, test=0.529) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.930, test=0.568) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.930, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.935, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.933, test=0.537) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.938, test=0.544) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.936, test=0.535) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.932, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.934, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.934, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.933, test=0.530) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.930, test=0.568) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.930, test=0.561) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.935, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.933, test=0.537) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.938, test=0.544) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.936, test=0.535) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.932, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.934, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.934, test=0.564) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.933, test=0.530) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.930, test=0.568) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.930, test=0.561) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.935, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.933, test=0.537) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.938, test=0.544) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.935, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.932, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.934, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.934, test=0.564) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.933, test=0.530) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.930, test=0.568) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.929, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.935, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.933, test=0.537) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.938, test=0.544) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.935, test=0.536) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.931, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.933, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.933, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.933, test=0.530) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.930, test=0.569) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.929, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.935, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.932, test=0.537) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.938, test=0.544) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.935, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.931, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.933, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.933, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.933, test=0.530) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.930, test=0.569) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.929, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.934, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.932, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.938, test=0.544) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.935, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.931, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.932, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.933, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.933, test=0.530) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.929, test=0.570) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.929, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.934, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.932, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.938, test=0.545) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.935, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.931, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.932, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.933, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.932, test=0.531) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.929, test=0.570) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.929, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.934, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.938, test=0.545) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.931, test=0.533) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.933, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.531) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.938, test=0.545) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.931, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.933, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.531) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.938, test=0.545) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.931, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.933, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.531) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.932, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.938, test=0.545) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.933, test=0.535) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.931, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.931, test=0.563) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.932, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.932, test=0.531) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.928, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.928, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.933, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.932, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.938, test=0.546) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.933, test=0.535) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.931, test=0.563) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.932, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.932, test=0.531) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.928, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.928, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.933, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.931, test=0.538) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.938, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.932, test=0.536) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.929, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.930, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.932, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.931, test=0.531) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.927, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.926, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.932, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.931, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.938, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.932, test=0.537) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.929, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.930, test=0.565) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.932, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.931, test=0.531) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.927, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.926, test=0.564) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.932, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.931, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.938, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.537) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.929, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.930, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.931, test=0.531) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.927, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.926, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.931, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.938, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.537) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.929, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.930, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.564) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.931, test=0.531) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.927, test=0.574) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.926, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.931, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.938, test=0.547) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.931, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.928, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.930, test=0.564) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.931, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.931, test=0.531) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.926, test=0.573) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.926, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.931, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.931, test=0.538) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.938, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.930, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.927, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.930, test=0.565) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.931, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.930, test=0.531) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.926, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.925, test=0.565) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.931, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.929, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.937, test=0.548) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.930, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.927, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.929, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.931, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.926, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.925, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.931, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.929, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.937, test=0.548) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.930, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.926, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.929, test=0.567) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.931, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.926, test=0.572) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.930, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.929, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.937, test=0.548) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.930, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.926, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.929, test=0.567) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.931, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.926, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.930, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.929, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.937, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.930, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.926, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.929, test=0.567) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.931, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.926, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.930, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.929, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.937, test=0.547) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.930, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.926, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.929, test=0.567) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.931, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.926, test=0.572) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.930, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.929, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.937, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.930, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.926, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.929, test=0.567) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.931, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.926, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.930, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.929, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.937, test=0.547) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.930, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.926, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.929, test=0.567) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.931, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.926, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.930, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.929, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.937, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.930, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.926, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.928, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.931, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.926, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.930, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.928, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.937, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.930, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.926, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.928, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.930, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.925, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.930, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.928, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.937, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.930, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.926, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.928, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.930, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.930, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.925, test=0.572) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.930, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.928, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.937, test=0.547) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.930, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.926, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.928, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.930, test=0.565) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.929, test=0.532) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.925, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.930, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.928, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.937, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.930, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.926, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.928, test=0.566) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.930, test=0.565) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.929, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.925, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.930, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.927, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.936, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.930, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.925, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.928, test=0.567) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.930, test=0.565) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.929, test=0.532) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.925, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.922, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.929, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.927, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.936, test=0.547) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.929, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.925, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.928, test=0.567) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.930, test=0.565) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.929, test=0.534) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.925, test=0.572) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.922, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.928, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.927, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.936, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.929, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.925, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.927, test=0.569) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.929, test=0.564) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.929, test=0.534) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.925, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.922, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.928, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.000108843537414966;, score=(train=0.924, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.000108843537414966;, score=(train=0.933, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.000108843537414966;, score=(train=0.925, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.000108843537414966;, score=(train=0.922, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.000108843537414966;, score=(train=0.924, test=0.574) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000108843537414966;, score=(train=0.926, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.000108843537414966;, score=(train=0.925, test=0.535) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000108843537414966;, score=(train=0.922, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.000108843537414966;, score=(train=0.919, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000108843537414966;, score=(train=0.926, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.000108843537414966;, score=(train=0.924, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.000108843537414966;, score=(train=0.933, test=0.547) total time=   0.4s
[CV 3/10] END ccp_alpha=0.000108843537414966;, score=(train=0.925, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.000108843537414966;, score=(train=0.922, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.000108843537414966;, score=(train=0.924, test=0.574) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000108843537414966;, score=(train=0.926, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000108843537414966;, score=(train=0.925, test=0.535) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000108843537414966;, score=(train=0.922, test=0.572) total time=   0.4s
[CV 9/10] END ccp_alpha=0.000108843537414966;, score=(train=0.919, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000108843537414966;, score=(train=0.926, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.924, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.933, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.925, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.922, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.924, test=0.574) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.926, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.925, test=0.535) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.922, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.919, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.926, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.924, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.933, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.925, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.922, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.924, test=0.573) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.926, test=0.562) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.925, test=0.536) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.922, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.919, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.925, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.924, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.933, test=0.547) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.925, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.922, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.924, test=0.573) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.926, test=0.562) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.925, test=0.536) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.922, test=0.572) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.919, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.925, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.924, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.932, test=0.546) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.925, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.921, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.924, test=0.573) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.926, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.924, test=0.536) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.922, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.919, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.925, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.924, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.932, test=0.546) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.925, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.921, test=0.529) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.924, test=0.573) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.926, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.924, test=0.536) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.922, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.919, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.925, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.924, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.932, test=0.546) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.925, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.921, test=0.529) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.924, test=0.573) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.926, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.923, test=0.537) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.922, test=0.574) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.918, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.925, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.924, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.932, test=0.546) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.925, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.921, test=0.529) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.924, test=0.573) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.926, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.922, test=0.539) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.922, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.918, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.925, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.924, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.932, test=0.546) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.925, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.921, test=0.529) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.924, test=0.573) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.926, test=0.563) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.922, test=0.539) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.922, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.918, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.925, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.924, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.931, test=0.546) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.924, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.920, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.923, test=0.573) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.925, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.921, test=0.540) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.921, test=0.574) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.918, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.924, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.000109624060150376;, score=(train=0.924, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.000109624060150376;, score=(train=0.931, test=0.546) total time=   0.3s
[CV 3/10] END ccp_alpha=0.000109624060150376;, score=(train=0.924, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.000109624060150376;, score=(train=0.920, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.000109624060150376;, score=(train=0.923, test=0.573) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000109624060150376;, score=(train=0.925, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000109624060150376;, score=(train=0.921, test=0.540) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000109624060150376;, score=(train=0.921, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.000109624060150376;, score=(train=0.918, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000109624060150376;, score=(train=0.924, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.924, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.931, test=0.546) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.924, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.920, test=0.529) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.923, test=0.574) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.925, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.920, test=0.540) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.921, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.918, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.924, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.924, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.931, test=0.546) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.924, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.920, test=0.529) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.923, test=0.574) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.925, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.920, test=0.540) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.921, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.918, test=0.564) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.924, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.924, test=0.539) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.931, test=0.546) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.924, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.920, test=0.529) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.923, test=0.574) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.925, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.920, test=0.540) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.921, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.918, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.924, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.923, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.931, test=0.546) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.923, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.920, test=0.529) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.923, test=0.575) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.925, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.920, test=0.540) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.921, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.918, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.924, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.921, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.930, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.921, test=0.538) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.919, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.922, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.924, test=0.563) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.918, test=0.544) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.919, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.916, test=0.563) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.923, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.921, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.930, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.921, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.919, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.922, test=0.576) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.918, test=0.544) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.919, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.916, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.923, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.921, test=0.540) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.930, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.921, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.919, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.922, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.918, test=0.544) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.919, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.916, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.923, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.921, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.930, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.921, test=0.538) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.919, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.922, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.918, test=0.544) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.919, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.916, test=0.563) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.923, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.921, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.930, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.921, test=0.538) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.919, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.922, test=0.576) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.918, test=0.544) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.919, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.916, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.923, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.921, test=0.540) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.930, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.918, test=0.529) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.922, test=0.576) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.918, test=0.544) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.919, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.916, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.923, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.921, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.930, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.920, test=0.539) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.918, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.922, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.918, test=0.544) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.919, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.916, test=0.563) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.923, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.920, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.929, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.918, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.922, test=0.576) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.918, test=0.543) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.919, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.916, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.923, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.920, test=0.540) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.929, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.918, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.922, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.924, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.918, test=0.543) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.919, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.916, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.923, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.929, test=0.548) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.916, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.921, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.918, test=0.542) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.918, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.922, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.929, test=0.548) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.916, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.921, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.923, test=0.564) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.918, test=0.542) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.918, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.922, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.929, test=0.548) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.916, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.921, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.918, test=0.542) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.918, test=0.577) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.922, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.929, test=0.548) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.916, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.921, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.918, test=0.542) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.918, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.922, test=0.530) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.927, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.921, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.923, test=0.564) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.917, test=0.542) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.918, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.922, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.927, test=0.547) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.921, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.917, test=0.542) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.918, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.922, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.927, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.530) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.921, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.917, test=0.542) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.918, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.922, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.927, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.530) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.921, test=0.576) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.917, test=0.542) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.918, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.922, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.919, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.927, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.915, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.921, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.923, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.917, test=0.542) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.918, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.921, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.919, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.927, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.920, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.915, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.921, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.922, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.917, test=0.542) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.918, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.921, test=0.529) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.919, test=0.539) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.927, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.919, test=0.539) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.915, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.920, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.922, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.917, test=0.542) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.917, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.916, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.921, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.919, test=0.539) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.926, test=0.547) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.919, test=0.539) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.915, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.920, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.922, test=0.564) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.917, test=0.543) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.917, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.915, test=0.562) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.921, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.916, test=0.542) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.924, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.916, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.911, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.918, test=0.579) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.918, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.911, test=0.547) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.916, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.914, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.917, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.916, test=0.542) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.924, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.915, test=0.534) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.911, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.917, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.918, test=0.567) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.911, test=0.547) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.916, test=0.579) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.914, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.917, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.916, test=0.542) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.924, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.915, test=0.534) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.911, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.917, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.918, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.911, test=0.547) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.915, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.912, test=0.563) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.916, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.916, test=0.542) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.924, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.915, test=0.534) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.911, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.917, test=0.579) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.918, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.911, test=0.547) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.915, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.912, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.916, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.916, test=0.542) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.924, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.915, test=0.534) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.911, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.917, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.918, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.911, test=0.547) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.915, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.912, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.916, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.916, test=0.542) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.924, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.914, test=0.534) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.911, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.917, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.917, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.910, test=0.547) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.915, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.912, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.916, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.916, test=0.542) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.924, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.914, test=0.534) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.911, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.917, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.917, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.910, test=0.547) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.915, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.912, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.916, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.915, test=0.543) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.924, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.914, test=0.534) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.911, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.917, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.917, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.910, test=0.547) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.915, test=0.578) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.912, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.916, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.915, test=0.543) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.924, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.914, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.910, test=0.532) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.916, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.917, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.909, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.915, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.912, test=0.563) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.914, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.915, test=0.543) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.923, test=0.550) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.913, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.916, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.916, test=0.568) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.909, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.914, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.912, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.914, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.915, test=0.543) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.923, test=0.549) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.913, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.916, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.916, test=0.568) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.909, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.914, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.912, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.914, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.915, test=0.543) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.923, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.913, test=0.533) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.916, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.916, test=0.568) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.909, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.914, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.912, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.914, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.915, test=0.543) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.923, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.913, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.916, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.916, test=0.569) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.909, test=0.548) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.914, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.912, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.914, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.915, test=0.543) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.923, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.913, test=0.531) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.916, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.916, test=0.569) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.909, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.914, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.912, test=0.564) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.914, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.915, test=0.543) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.923, test=0.549) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.913, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.916, test=0.579) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.916, test=0.569) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.909, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.914, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.912, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.914, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.914, test=0.544) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.923, test=0.550) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.913, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.916, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.916, test=0.568) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.909, test=0.548) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.914, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.912, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.913, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.914, test=0.544) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.923, test=0.550) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.913, test=0.531) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.916, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.916, test=0.568) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.909, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.914, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.912, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.913, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.914, test=0.544) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.923, test=0.550) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.913, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.916, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.916, test=0.568) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.909, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.914, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.912, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.913, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.914, test=0.544) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.923, test=0.550) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.913, test=0.531) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.916, test=0.579) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.916, test=0.568) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.909, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.914, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.912, test=0.564) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.913, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011283214140357;, score=(train=0.914, test=0.544) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011283214140357;, score=(train=0.922, test=0.550) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011283214140357;, score=(train=0.912, test=0.532) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011283214140357;, score=(train=0.910, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011283214140357;, score=(train=0.914, test=0.580) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011283214140357;, score=(train=0.915, test=0.568) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011283214140357;, score=(train=0.908, test=0.547) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011283214140357;, score=(train=0.913, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011283214140357;, score=(train=0.911, test=0.565) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011283214140357;, score=(train=0.912, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.912, test=0.542) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.920, test=0.554) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.910, test=0.535) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.907, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.912, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.913, test=0.569) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.905, test=0.546) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.910, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.909, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.909, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.912, test=0.542) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.919, test=0.554) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.909, test=0.536) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.907, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.912, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.912, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.905, test=0.546) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.910, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.909, test=0.566) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.909, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.912, test=0.543) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.919, test=0.554) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.909, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.907, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.912, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.912, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.905, test=0.546) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.910, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.909, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.909, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.912, test=0.543) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.919, test=0.554) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.909, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.907, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.911, test=0.582) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.912, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.905, test=0.546) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.910, test=0.577) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.909, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.909, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.912, test=0.543) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.919, test=0.554) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.909, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.907, test=0.531) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.911, test=0.582) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.912, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.905, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.910, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.908, test=0.565) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.909, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.911, test=0.545) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.919, test=0.554) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.909, test=0.536) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.906, test=0.531) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.910, test=0.584) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.909, test=0.569) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.905, test=0.546) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.910, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.907, test=0.565) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.908, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.911, test=0.545) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.917, test=0.555) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.909, test=0.537) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.906, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.909, test=0.583) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.907, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.903, test=0.544) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.909, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.906, test=0.565) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.907, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.911, test=0.545) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.917, test=0.555) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.909, test=0.537) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.906, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.909, test=0.583) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.907, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.903, test=0.544) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.909, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.906, test=0.565) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.907, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.910, test=0.545) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.917, test=0.555) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.909, test=0.537) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.906, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.909, test=0.583) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.907, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.903, test=0.544) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.909, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.906, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.906, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.910, test=0.545) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.917, test=0.554) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.908, test=0.538) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.905, test=0.534) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.908, test=0.583) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.907, test=0.566) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.903, test=0.544) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.908, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.906, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.906, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.910, test=0.544) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.916, test=0.554) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.908, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.905, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.908, test=0.583) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.906, test=0.565) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.903, test=0.546) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.907, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.904, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.905, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.910, test=0.544) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.916, test=0.554) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.908, test=0.539) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.905, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.908, test=0.583) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.906, test=0.565) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.903, test=0.546) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.907, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.904, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.905, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.908, test=0.546) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.915, test=0.554) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.905, test=0.541) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.903, test=0.534) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.905, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.905, test=0.566) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.901, test=0.547) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.905, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.903, test=0.565) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.902, test=0.529) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.907, test=0.547) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.914, test=0.555) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.905, test=0.542) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.901, test=0.537) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.904, test=0.580) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.904, test=0.566) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.900, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.904, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.902, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.902, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.907, test=0.547) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.914, test=0.555) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.905, test=0.542) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.901, test=0.537) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.904, test=0.580) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.904, test=0.566) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.900, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.903, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.902, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.902, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.907, test=0.547) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.913, test=0.555) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.904, test=0.542) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.901, test=0.537) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.904, test=0.580) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.903, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.900, test=0.548) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.903, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.902, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.902, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.907, test=0.546) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.913, test=0.555) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.904, test=0.543) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.901, test=0.537) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.903, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.903, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.900, test=0.548) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.903, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.902, test=0.566) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.901, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.906, test=0.546) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.912, test=0.555) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.903, test=0.544) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.899, test=0.537) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.902, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.902, test=0.566) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.899, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.902, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.901, test=0.567) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.900, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.904, test=0.544) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.912, test=0.556) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.903, test=0.544) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.898, test=0.537) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.900, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.901, test=0.566) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.898, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.902, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.899, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.899, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.904, test=0.544) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.912, test=0.556) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.903, test=0.544) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.898, test=0.537) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.900, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.901, test=0.566) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.898, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.902, test=0.577) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.899, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.899, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.902, test=0.544) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.910, test=0.556) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.902, test=0.544) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.895, test=0.539) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.899, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.899, test=0.567) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.897, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.900, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.896, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.898, test=0.528) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.902, test=0.544) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.910, test=0.556) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.902, test=0.544) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.895, test=0.539) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.899, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.899, test=0.567) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.897, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.900, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.896, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.897, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.901, test=0.545) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.910, test=0.556) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.902, test=0.544) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.895, test=0.539) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.899, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.898, test=0.567) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.897, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.899, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.896, test=0.571) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.897, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.900, test=0.546) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.909, test=0.555) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.901, test=0.543) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.895, test=0.539) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.899, test=0.578) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.898, test=0.568) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.897, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.899, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.895, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.897, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.900, test=0.546) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.909, test=0.556) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.901, test=0.543) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.895, test=0.539) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.899, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.898, test=0.568) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.897, test=0.550) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.899, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.895, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.897, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.900, test=0.546) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.909, test=0.556) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.901, test=0.543) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.895, test=0.539) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.899, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.898, test=0.568) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.897, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.899, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.895, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.897, test=0.529) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.547) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.908, test=0.557) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.901, test=0.544) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.539) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.898, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.571) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.896, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.547) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.908, test=0.557) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.901, test=0.544) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.539) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.898, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.896, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.547) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.908, test=0.557) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.901, test=0.544) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.539) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.898, test=0.578) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.549) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.896, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.899, test=0.547) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.908, test=0.557) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.901, test=0.544) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.894, test=0.539) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.897, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.897, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.897, test=0.549) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.898, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.894, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.896, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.899, test=0.547) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.908, test=0.557) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.901, test=0.544) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.894, test=0.541) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.897, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.897, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.897, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.898, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.893, test=0.572) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.896, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.899, test=0.547) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.907, test=0.556) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.901, test=0.544) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.893, test=0.540) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.897, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.897, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.897, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.897, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.893, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.895, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.898, test=0.548) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.906, test=0.560) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.901, test=0.545) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.892, test=0.541) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.897, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.897, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.897, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.897, test=0.571) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.890, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.895, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.898, test=0.548) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.906, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.901, test=0.545) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.892, test=0.541) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.897, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.897, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.897, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.897, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.890, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.894, test=0.526) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.898, test=0.548) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.906, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.901, test=0.545) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.892, test=0.541) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.897, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.897, test=0.571) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.897, test=0.549) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.896, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.890, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.893, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.898, test=0.548) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.904, test=0.559) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.900, test=0.544) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.892, test=0.541) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.896, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.896, test=0.573) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.896, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.895, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.890, test=0.572) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.893, test=0.525) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.898, test=0.548) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.904, test=0.559) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.900, test=0.544) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.892, test=0.541) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.896, test=0.577) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.896, test=0.572) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.896, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.895, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.890, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.893, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.898, test=0.548) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.903, test=0.559) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.900, test=0.544) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.892, test=0.541) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.896, test=0.577) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.896, test=0.572) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.896, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.895, test=0.572) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.888, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.893, test=0.526) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.896, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.903, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.899, test=0.545) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.890, test=0.542) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.894, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.893, test=0.574) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.896, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.894, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.886, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.891, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.896, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.903, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.899, test=0.545) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.890, test=0.542) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.892, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.893, test=0.574) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.896, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.894, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.886, test=0.572) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.890, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.896, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.903, test=0.560) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.899, test=0.545) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.890, test=0.542) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.892, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.893, test=0.574) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.896, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.894, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.886, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.890, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.896, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.903, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.899, test=0.545) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.890, test=0.542) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.892, test=0.579) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.893, test=0.574) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.896, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.894, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.886, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.890, test=0.524) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.896, test=0.550) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.903, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.899, test=0.545) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.888, test=0.545) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.892, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.892, test=0.574) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.896, test=0.549) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.893, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.885, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.890, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.896, test=0.550) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.903, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.898, test=0.545) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.887, test=0.545) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.891, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.892, test=0.574) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.896, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.893, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.885, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.890, test=0.523) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.896, test=0.550) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.903, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.898, test=0.545) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.887, test=0.545) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.891, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.892, test=0.574) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.896, test=0.549) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.893, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.885, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.890, test=0.523) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.895, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.900, test=0.561) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.898, test=0.547) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.886, test=0.547) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.890, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.890, test=0.574) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.894, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.892, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.884, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.889, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.895, test=0.549) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.900, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.898, test=0.547) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.886, test=0.547) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.890, test=0.579) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.890, test=0.574) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.894, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.892, test=0.571) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.884, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.889, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.894, test=0.549) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.899, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.897, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.890, test=0.580) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.894, test=0.552) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.891, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.882, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.888, test=0.525) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.894, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.899, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.897, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.890, test=0.580) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.894, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.891, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.882, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.888, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.894, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.899, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.897, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.890, test=0.580) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.889, test=0.575) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.894, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.891, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.882, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.888, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.894, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.899, test=0.560) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.897, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.889, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.894, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.891, test=0.572) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.882, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.888, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.894, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.898, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.897, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.889, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.894, test=0.551) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.891, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.881, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.888, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.893, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.898, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.896, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.889, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.894, test=0.551) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.890, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.881, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.888, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.893, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.898, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.896, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.889, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.894, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.890, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.881, test=0.570) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.888, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.893, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.898, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.896, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.889, test=0.578) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.894, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.890, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.881, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.888, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.893, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.898, test=0.562) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.896, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.889, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.894, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.890, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.881, test=0.570) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.888, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.893, test=0.550) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.898, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.896, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.885, test=0.546) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.888, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.894, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.890, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.881, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.888, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.893, test=0.550) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.898, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.896, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.888, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.889, test=0.575) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.894, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.890, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.881, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.888, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.893, test=0.550) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.898, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.896, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.888, test=0.578) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.894, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.890, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.881, test=0.570) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.888, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.893, test=0.549) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.898, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.896, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.888, test=0.579) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.889, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.894, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.890, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.881, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.888, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.893, test=0.550) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.897, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.895, test=0.554) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.885, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.887, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.888, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.893, test=0.551) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.887, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.881, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.887, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.893, test=0.550) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.897, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.895, test=0.554) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.885, test=0.546) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.887, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.888, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.893, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.887, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.881, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.887, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.892, test=0.551) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.897, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.895, test=0.554) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.885, test=0.547) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.887, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.888, test=0.575) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.893, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.886, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.881, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.887, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.891, test=0.551) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.897, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.895, test=0.554) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.884, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.886, test=0.579) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.887, test=0.574) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.892, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.885, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.881, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.887, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.890, test=0.552) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.897, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.893, test=0.555) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.884, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.886, test=0.579) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.887, test=0.574) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.892, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.885, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.880, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.887, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.889, test=0.553) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.897, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.893, test=0.555) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.884, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.886, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.887, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.892, test=0.552) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.885, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.880, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.887, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.889, test=0.554) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.897, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.893, test=0.555) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.884, test=0.549) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.886, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.887, test=0.575) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.892, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.885, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.880, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.887, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.888, test=0.555) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.896, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.892, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.883, test=0.548) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.886, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.886, test=0.578) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.891, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.884, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.880, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.886, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.888, test=0.555) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.896, test=0.562) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.892, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.883, test=0.548) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.885, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.886, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.891, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.884, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.880, test=0.569) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.886, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.888, test=0.555) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.896, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.892, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.883, test=0.548) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.885, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.886, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.891, test=0.552) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.883, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.879, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.885, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.888, test=0.555) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.896, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.892, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.883, test=0.548) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.884, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.886, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.891, test=0.552) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.883, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.879, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.885, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.888, test=0.555) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.896, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.892, test=0.556) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.883, test=0.548) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.884, test=0.579) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.886, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.891, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.883, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.879, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.885, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.888, test=0.555) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.895, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.892, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.882, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.884, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.886, test=0.578) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.890, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.883, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.878, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.885, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.888, test=0.555) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.895, test=0.562) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.892, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.882, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.884, test=0.581) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.886, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.890, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.883, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.878, test=0.569) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.885, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.887, test=0.555) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.895, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.892, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.882, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.883, test=0.581) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.886, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.890, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.882, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.878, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.885, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.887, test=0.555) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.895, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.892, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.882, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.883, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.886, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.890, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.882, test=0.574) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.878, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.885, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.887, test=0.555) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.895, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.892, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.882, test=0.550) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.883, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.886, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.890, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.882, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.878, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.885, test=0.527) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.886, test=0.555) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.895, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.892, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.882, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.883, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.885, test=0.578) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.890, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.882, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.877, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.884, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.886, test=0.554) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.895, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.891, test=0.556) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.882, test=0.550) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.883, test=0.581) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.885, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.890, test=0.552) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.882, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.877, test=0.570) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.884, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.886, test=0.554) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.895, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.891, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.882, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.883, test=0.581) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.885, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.890, test=0.552) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.882, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.877, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.884, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.886, test=0.554) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.894, test=0.561) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.891, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.882, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.883, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.885, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.890, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.882, test=0.573) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.877, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.884, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.886, test=0.554) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.894, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.891, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.882, test=0.550) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.883, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.885, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.890, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.882, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.877, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.884, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.885, test=0.554) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.894, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.891, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.882, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.882, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.884, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.890, test=0.553) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.881, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.877, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.883, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.885, test=0.554) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.894, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.891, test=0.556) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.882, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.882, test=0.581) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.884, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.890, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.881, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.877, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.883, test=0.527) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.885, test=0.556) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.891, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.881, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.882, test=0.582) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.884, test=0.579) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.889, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.881, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.876, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.883, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.885, test=0.556) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.893, test=0.561) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.890, test=0.556) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.881, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.882, test=0.582) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.884, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.889, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.881, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.876, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.883, test=0.528) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.884, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.889, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.879, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.880, test=0.584) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.884, test=0.579) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.889, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.880, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.875, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.881, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.884, test=0.557) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.889, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.879, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.880, test=0.584) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.883, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.889, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.880, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.875, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.881, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.884, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.889, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.879, test=0.550) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.879, test=0.584) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.883, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.889, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.880, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.875, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.879, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.883, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.889, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.878, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.882, test=0.580) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.889, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.879, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.874, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.878, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.883, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.889, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.878, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.882, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.889, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.879, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.874, test=0.569) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.878, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.883, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.889, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.878, test=0.586) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.882, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.889, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.879, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.874, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.878, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.883, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.893, test=0.561) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.889, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.878, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.882, test=0.580) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.889, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.879, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.873, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.878, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.883, test=0.557) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.889, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.878, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.882, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.889, test=0.553) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.879, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.873, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.878, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.883, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.889, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.878, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.882, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.889, test=0.553) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.879, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.873, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.878, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.883, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.893, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.889, test=0.558) total time=   0.5s
[CV 4/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.878, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.882, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.879, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.873, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.878, test=0.530) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.883, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.893, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.888, test=0.557) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.878, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.881, test=0.579) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.879, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.873, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.878, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.882, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.893, test=0.561) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.888, test=0.557) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.878, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.881, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.879, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.873, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.878, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.882, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.888, test=0.557) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.878, test=0.587) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.881, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.879, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.873, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.878, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.882, test=0.558) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.888, test=0.557) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.878, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.881, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.879, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.872, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.878, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.882, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.888, test=0.557) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.877, test=0.549) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.878, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.881, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.879, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.872, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.878, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.882, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.888, test=0.557) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.877, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.878, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.881, test=0.579) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.879, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.872, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.878, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.881, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.887, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.877, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.877, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.880, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.876, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.872, test=0.568) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.876, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.881, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.887, test=0.559) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.877, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.876, test=0.585) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.880, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.876, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.872, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.876, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.881, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.887, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.877, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.876, test=0.585) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.880, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.876, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.872, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.876, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.881, test=0.557) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.887, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.877, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.876, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.880, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.876, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.872, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.876, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.881, test=0.557) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.887, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.877, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.875, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.880, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.888, test=0.553) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.876, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.871, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.876, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.881, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.887, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.877, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.875, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.880, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.876, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.871, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.876, test=0.533) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.881, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.887, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.877, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.875, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.880, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.888, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.876, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.871, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.876, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.880, test=0.556) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.887, test=0.557) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.875, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.879, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.887, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.876, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.871, test=0.569) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.876, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.880, test=0.556) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.893, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.887, test=0.557) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.877, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.875, test=0.586) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.879, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.887, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.876, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.871, test=0.570) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.876, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.879, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.892, test=0.561) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.887, test=0.557) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.876, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.875, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.878, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.887, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.875, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.871, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.876, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.878, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.892, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.886, test=0.557) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.875, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.875, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.878, test=0.578) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.887, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.875, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.870, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.875, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.878, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.892, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.886, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.875, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.875, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.878, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.887, test=0.553) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.875, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.870, test=0.569) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.875, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.878, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.892, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.886, test=0.558) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.875, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.875, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.878, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.887, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.875, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.870, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.875, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.878, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.892, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.886, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.875, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.875, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.878, test=0.579) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.887, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.875, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.870, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.875, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.878, test=0.557) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.891, test=0.561) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.886, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.874, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.875, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.878, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.887, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.875, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.870, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.875, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.877, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.890, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.885, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.874, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.874, test=0.587) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.878, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.887, test=0.553) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.875, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.869, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.874, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.874, test=0.559) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.889, test=0.563) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.884, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.874, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.874, test=0.588) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.877, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.887, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.874, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.869, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.873, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.873, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.888, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.884, test=0.559) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.874, test=0.550) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.873, test=0.588) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.877, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.886, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.874, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.869, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.873, test=0.532) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.873, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.888, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.884, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.873, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.873, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.877, test=0.579) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.886, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.874, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.869, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.873, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.873, test=0.558) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.887, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.883, test=0.558) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.873, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.869, test=0.588) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.876, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.885, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.873, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.866, test=0.568) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.873, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.873, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.887, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.883, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.873, test=0.549) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.869, test=0.588) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.876, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.885, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.873, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.866, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.873, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.873, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.886, test=0.565) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.882, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.872, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.869, test=0.588) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.875, test=0.581) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.885, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.873, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.866, test=0.569) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.872, test=0.532) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.873, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.885, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.882, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.872, test=0.549) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.869, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.875, test=0.581) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.885, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.871, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.865, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.870, test=0.535) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.873, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.885, test=0.565) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.882, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.872, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.868, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.875, test=0.581) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.885, test=0.554) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.871, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.865, test=0.568) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.870, test=0.535) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.873, test=0.558) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.885, test=0.565) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.880, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.871, test=0.548) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.868, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.875, test=0.581) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.883, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.871, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.864, test=0.570) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.870, test=0.535) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.873, test=0.559) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.883, test=0.563) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.878, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.870, test=0.548) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.867, test=0.585) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.873, test=0.581) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.882, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.868, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.862, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.869, test=0.535) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.872, test=0.560) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.883, test=0.563) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.878, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.870, test=0.548) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.867, test=0.585) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.873, test=0.581) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.882, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.868, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.862, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.869, test=0.535) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.872, test=0.560) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.882, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.878, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.870, test=0.547) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.866, test=0.585) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.873, test=0.581) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.881, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.868, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.862, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.869, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.871, test=0.562) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.882, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.878, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.869, test=0.547) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.866, test=0.585) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.873, test=0.581) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.881, test=0.556) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.868, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.862, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.869, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.870, test=0.562) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.881, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.878, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.869, test=0.547) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.866, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.872, test=0.581) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.880, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.868, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.859, test=0.574) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.869, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.870, test=0.562) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.881, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.878, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.869, test=0.547) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.866, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.872, test=0.581) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.880, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.868, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.859, test=0.574) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.869, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.870, test=0.562) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.881, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.878, test=0.560) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.868, test=0.547) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.866, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.872, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.880, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.868, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.859, test=0.574) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.869, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.870, test=0.562) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.881, test=0.561) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.878, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.868, test=0.547) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.866, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.872, test=0.580) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.880, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.868, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.859, test=0.574) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.869, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.869, test=0.563) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.881, test=0.561) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.878, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.868, test=0.547) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.866, test=0.586) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.872, test=0.580) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.880, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.868, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.859, test=0.574) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.869, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.868, test=0.561) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.879, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.875, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.866, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.863, test=0.587) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.870, test=0.582) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.879, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.865, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.858, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.868, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.868, test=0.561) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.879, test=0.560) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.875, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.866, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.863, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.870, test=0.582) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.879, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.865, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.858, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.868, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.868, test=0.562) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.878, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.874, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.863, test=0.549) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.859, test=0.588) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.868, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.878, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.864, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.857, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.868, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.867, test=0.563) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.878, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.874, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.863, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.859, test=0.588) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.868, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.878, test=0.556) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.864, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.857, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.868, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.865, test=0.562) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.878, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.873, test=0.560) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.863, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.859, test=0.588) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.868, test=0.582) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.878, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.863, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.856, test=0.572) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.868, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.865, test=0.562) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.878, test=0.561) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.873, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.863, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.859, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.868, test=0.582) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.878, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.863, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.856, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.868, test=0.537) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.865, test=0.562) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.878, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.873, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.863, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.859, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.867, test=0.582) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.878, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.863, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.855, test=0.572) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.867, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.865, test=0.562) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.878, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.873, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.863, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.859, test=0.587) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.867, test=0.582) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.878, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.863, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.855, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.867, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.865, test=0.562) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.878, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.873, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.863, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.859, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.867, test=0.582) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.878, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.863, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.855, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.867, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.865, test=0.562) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.878, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.873, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.862, test=0.549) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.859, test=0.588) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.867, test=0.582) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.878, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.863, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.855, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.867, test=0.535) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.865, test=0.562) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.878, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.873, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.862, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.859, test=0.587) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.867, test=0.582) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.878, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.863, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.855, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.867, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.865, test=0.563) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.878, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.873, test=0.560) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.862, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.858, test=0.588) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.866, test=0.581) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.878, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.863, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.854, test=0.572) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.867, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.865, test=0.564) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.878, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.872, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.862, test=0.549) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.858, test=0.588) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.865, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.878, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.862, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.854, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.867, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.864, test=0.564) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.878, test=0.562) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.872, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.862, test=0.550) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.858, test=0.589) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.865, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.877, test=0.557) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.861, test=0.577) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.854, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.867, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.864, test=0.564) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.878, test=0.562) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.872, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.861, test=0.550) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.858, test=0.589) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.865, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.876, test=0.557) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.861, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.854, test=0.572) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.867, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.864, test=0.563) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.877, test=0.563) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.871, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.861, test=0.551) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.857, test=0.589) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.865, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.876, test=0.556) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.861, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.854, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.867, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.864, test=0.564) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.876, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.871, test=0.561) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.861, test=0.551) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.857, test=0.590) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.865, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.875, test=0.557) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.860, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.854, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.866, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.864, test=0.564) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.876, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.870, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.860, test=0.551) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.857, test=0.590) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.864, test=0.585) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.875, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.860, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.854, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.866, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.864, test=0.564) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.876, test=0.564) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.868, test=0.563) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.860, test=0.551) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.856, test=0.590) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.864, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.875, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.859, test=0.577) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.852, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.866, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.863, test=0.564) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.876, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.868, test=0.563) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.860, test=0.553) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.856, test=0.591) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.864, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.874, test=0.557) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.859, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.852, test=0.574) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.866, test=0.536) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.863, test=0.564) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.876, test=0.564) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.867, test=0.564) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.860, test=0.553) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.856, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.864, test=0.584) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.874, test=0.557) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.859, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.852, test=0.574) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.866, test=0.536) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.862, test=0.564) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.875, test=0.566) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.866, test=0.563) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.859, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.855, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.863, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.873, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.858, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.852, test=0.574) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.865, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.862, test=0.564) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.875, test=0.566) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.866, test=0.563) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.859, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.855, test=0.590) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.863, test=0.585) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.873, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.858, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.852, test=0.574) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.865, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.862, test=0.564) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.875, test=0.566) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.865, test=0.563) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.859, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.855, test=0.590) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.863, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.873, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.858, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.851, test=0.573) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.865, test=0.537) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.861, test=0.565) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.874, test=0.566) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.864, test=0.564) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.858, test=0.553) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.854, test=0.591) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.863, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.872, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.858, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.850, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.865, test=0.537) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.861, test=0.565) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.874, test=0.566) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.864, test=0.564) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.858, test=0.553) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.854, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.863, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.872, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.858, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.850, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.865, test=0.537) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.861, test=0.565) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.873, test=0.568) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.864, test=0.564) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.858, test=0.554) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.854, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.863, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.871, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.858, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.850, test=0.571) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.865, test=0.537) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.860, test=0.565) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.871, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.863, test=0.564) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.857, test=0.555) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.853, test=0.593) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.862, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.870, test=0.553) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.858, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.848, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.865, test=0.537) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.860, test=0.565) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.871, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.863, test=0.564) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.856, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.853, test=0.592) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.862, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.867, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.858, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.848, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.865, test=0.537) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.859, test=0.565) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.871, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.862, test=0.563) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.856, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.852, test=0.592) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.862, test=0.586) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.867, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.857, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.848, test=0.573) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.865, test=0.537) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.859, test=0.566) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.871, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.862, test=0.563) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.856, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.852, test=0.592) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.860, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.866, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.857, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.847, test=0.572) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.864, test=0.539) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.858, test=0.566) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.868, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.859, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.855, test=0.553) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.851, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.860, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.864, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.855, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.846, test=0.575) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.863, test=0.538) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.858, test=0.566) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.868, test=0.572) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.859, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.855, test=0.553) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.851, test=0.592) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.859, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.864, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.855, test=0.578) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.845, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.862, test=0.537) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.858, test=0.566) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.868, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.859, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.855, test=0.553) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.851, test=0.592) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.859, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.864, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.855, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.845, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.862, test=0.537) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.858, test=0.566) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.868, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.859, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.855, test=0.553) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.851, test=0.592) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.859, test=0.584) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.864, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.855, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.845, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.861, test=0.539) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.858, test=0.566) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.868, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.859, test=0.562) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.855, test=0.553) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.851, test=0.592) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.859, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.864, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.855, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.845, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.861, test=0.539) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.858, test=0.566) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.868, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.858, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.855, test=0.553) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.851, test=0.592) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.859, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.864, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.855, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.844, test=0.576) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.861, test=0.539) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.858, test=0.566) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.867, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.858, test=0.562) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.855, test=0.553) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.851, test=0.593) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.859, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.864, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.855, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.844, test=0.575) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.861, test=0.539) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.857, test=0.570) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.867, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.858, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.855, test=0.553) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.851, test=0.593) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.859, test=0.584) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.864, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.854, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.844, test=0.577) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.860, test=0.540) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.857, test=0.570) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.867, test=0.571) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.857, test=0.563) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.854, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.851, test=0.593) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.858, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.864, test=0.550) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.852, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.843, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.859, test=0.541) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.856, test=0.568) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.867, test=0.571) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.857, test=0.563) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.853, test=0.555) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.850, test=0.593) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.858, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.863, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.852, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.843, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.859, test=0.541) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.856, test=0.568) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.867, test=0.571) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.857, test=0.563) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.853, test=0.555) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.850, test=0.593) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.858, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.863, test=0.551) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.852, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.843, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.859, test=0.541) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.856, test=0.569) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.866, test=0.570) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.857, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.852, test=0.556) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.848, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.857, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.861, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.852, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.842, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.859, test=0.542) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.856, test=0.569) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.866, test=0.570) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.857, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.852, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.848, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.857, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.861, test=0.551) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.851, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.842, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.859, test=0.542) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.856, test=0.569) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.866, test=0.570) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.857, test=0.562) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.852, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.848, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.857, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.861, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.851, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.842, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.858, test=0.545) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.856, test=0.569) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.866, test=0.570) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.857, test=0.562) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.852, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.848, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.857, test=0.584) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.861, test=0.551) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.851, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.842, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.858, test=0.545) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.855, test=0.569) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.865, test=0.571) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.856, test=0.562) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.852, test=0.555) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.848, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.857, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.860, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.851, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.842, test=0.576) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.858, test=0.546) total time=   0.3s
[CV 1/10] END ccp_alpha=0.000134694894146949;, score=(train=0.855, test=0.569) total time=   0.4s
[CV 2/10] END ccp_alpha=0.000134694894146949;, score=(train=0.865, test=0.571) total time=   0.3s
[CV 3/10] END ccp_alpha=0.000134694894146949;, score=(train=0.856, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.000134694894146949;, score=(train=0.852, test=0.555) total time=   0.3s
[CV 5/10] END ccp_alpha=0.000134694894146949;, score=(train=0.848, test=0.591) total time=   0.4s
[CV 6/10] END ccp_alpha=0.000134694894146949;, score=(train=0.857, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000134694894146949;, score=(train=0.860, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000134694894146949;, score=(train=0.851, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.000134694894146949;, score=(train=0.842, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000134694894146949;, score=(train=0.858, test=0.546) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.855, test=0.569) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.865, test=0.571) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.856, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.852, test=0.555) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.848, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.857, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.860, test=0.553) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.851, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.842, test=0.576) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.858, test=0.546) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.854, test=0.569) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.865, test=0.571) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.856, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.852, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.848, test=0.591) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.857, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.860, test=0.554) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.851, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.842, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.858, test=0.546) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.854, test=0.569) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.865, test=0.571) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.856, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.851, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.848, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.857, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.860, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.851, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.842, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.858, test=0.546) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.853, test=0.571) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.865, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.856, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.851, test=0.554) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.847, test=0.590) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.857, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.859, test=0.554) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.851, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.842, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.858, test=0.546) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.853, test=0.571) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.865, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.854, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.851, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.847, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.857, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.859, test=0.554) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.851, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.842, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.857, test=0.546) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.853, test=0.571) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.865, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.854, test=0.560) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.851, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.847, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.857, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.859, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.851, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.842, test=0.576) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.857, test=0.546) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.853, test=0.571) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.865, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.854, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.851, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.847, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.857, test=0.585) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.859, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.851, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.842, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.857, test=0.546) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.853, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.865, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.853, test=0.559) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.851, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.847, test=0.591) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.856, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.858, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.850, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.841, test=0.576) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.857, test=0.546) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.853, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.865, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.853, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.851, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.847, test=0.591) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.856, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.858, test=0.554) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.850, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.841, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.856, test=0.547) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.853, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.864, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.852, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.851, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.843, test=0.592) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.856, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.857, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.850, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.840, test=0.575) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.856, test=0.547) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.851, test=0.570) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.863, test=0.572) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.852, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.851, test=0.554) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.841, test=0.593) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.855, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.857, test=0.555) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.849, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.840, test=0.575) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.856, test=0.547) total time=   0.3s
[CV 1/10] END ccp_alpha=0.000135969868173258;, score=(train=0.851, test=0.570) total time=   0.4s
[CV 2/10] END ccp_alpha=0.000135969868173258;, score=(train=0.863, test=0.573) total time=   0.3s
[CV 3/10] END ccp_alpha=0.000135969868173258;, score=(train=0.851, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.000135969868173258;, score=(train=0.850, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.000135969868173258;, score=(train=0.841, test=0.593) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000135969868173258;, score=(train=0.855, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000135969868173258;, score=(train=0.857, test=0.555) total time=   0.4s
[CV 8/10] END ccp_alpha=0.000135969868173258;, score=(train=0.849, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.000135969868173258;, score=(train=0.840, test=0.575) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000135969868173258;, score=(train=0.855, test=0.547) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.851, test=0.570) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.863, test=0.573) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.850, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.850, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.841, test=0.593) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.855, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.857, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.849, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.840, test=0.575) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.855, test=0.547) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.850, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.863, test=0.574) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.850, test=0.559) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.850, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.841, test=0.593) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.854, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.857, test=0.555) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.849, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.840, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.855, test=0.547) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.850, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.863, test=0.574) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.850, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.850, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.841, test=0.593) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.854, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.857, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.849, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.840, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.855, test=0.547) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.850, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.863, test=0.574) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.850, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.850, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.841, test=0.593) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.854, test=0.584) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.857, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.849, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.840, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.855, test=0.547) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.850, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.863, test=0.574) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.850, test=0.559) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.850, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.841, test=0.593) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.854, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.857, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.849, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.840, test=0.576) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.855, test=0.547) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.850, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.863, test=0.575) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.849, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.850, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.839, test=0.594) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.854, test=0.583) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.857, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.848, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.840, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.852, test=0.549) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.850, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.862, test=0.576) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.849, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.850, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.839, test=0.594) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.854, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.856, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.848, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.840, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.852, test=0.549) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.850, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.862, test=0.576) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.849, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.850, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.839, test=0.594) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.854, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.856, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.848, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.840, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.852, test=0.549) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.850, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.862, test=0.576) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.849, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.850, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.838, test=0.595) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.852, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.856, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.848, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.840, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.852, test=0.549) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.849, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.860, test=0.578) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.849, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.850, test=0.556) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.838, test=0.595) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.852, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.856, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.848, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.840, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.852, test=0.549) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.849, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.860, test=0.578) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.849, test=0.558) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.849, test=0.557) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.837, test=0.596) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.852, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.856, test=0.556) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.848, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.839, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.852, test=0.549) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.849, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.860, test=0.578) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.849, test=0.558) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.849, test=0.557) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.837, test=0.596) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.852, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.856, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.848, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.839, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.852, test=0.549) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.848, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.860, test=0.577) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.849, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.849, test=0.557) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.837, test=0.596) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.852, test=0.585) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.856, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.847, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.836, test=0.576) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.849, test=0.548) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.848, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.860, test=0.577) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.849, test=0.558) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.849, test=0.557) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.837, test=0.596) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.852, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.856, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.847, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.836, test=0.576) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.849, test=0.548) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.848, test=0.573) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.860, test=0.577) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.849, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.848, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.837, test=0.596) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.851, test=0.584) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.856, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.846, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.836, test=0.576) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.849, test=0.548) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.848, test=0.573) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.860, test=0.577) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.849, test=0.558) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.848, test=0.556) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.837, test=0.596) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.851, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.856, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.846, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.836, test=0.576) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.849, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000137375612080581;, score=(train=0.846, test=0.573) total time=   0.3s
[CV 2/10] END ccp_alpha=0.000137375612080581;, score=(train=0.858, test=0.577) total time=   0.3s
[CV 3/10] END ccp_alpha=0.000137375612080581;, score=(train=0.847, test=0.558) total time=   0.3s
[CV 4/10] END ccp_alpha=0.000137375612080581;, score=(train=0.847, test=0.558) total time=   0.3s
[CV 5/10] END ccp_alpha=0.000137375612080581;, score=(train=0.837, test=0.596) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000137375612080581;, score=(train=0.850, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000137375612080581;, score=(train=0.855, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000137375612080581;, score=(train=0.846, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.000137375612080581;, score=(train=0.835, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000137375612080581;, score=(train=0.848, test=0.548) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.845, test=0.572) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.858, test=0.577) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.846, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.847, test=0.558) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.837, test=0.596) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.850, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.854, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.846, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.835, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.848, test=0.548) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.845, test=0.573) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.858, test=0.577) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.846, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.846, test=0.558) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.837, test=0.596) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.849, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.854, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.846, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.835, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.847, test=0.548) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.844, test=0.573) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.858, test=0.577) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.846, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.846, test=0.558) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.837, test=0.596) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.849, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.854, test=0.555) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.846, test=0.578) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.835, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.847, test=0.548) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.843, test=0.573) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.858, test=0.578) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.845, test=0.559) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.846, test=0.558) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.836, test=0.598) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.849, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.853, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.846, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.835, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.847, test=0.548) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.842, test=0.574) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.856, test=0.576) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.844, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.845, test=0.561) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.836, test=0.597) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.848, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.853, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.846, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.834, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.846, test=0.550) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.842, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.856, test=0.576) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.844, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.845, test=0.561) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.836, test=0.597) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.848, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.852, test=0.556) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.846, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.834, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.846, test=0.550) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.842, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.856, test=0.575) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.844, test=0.560) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.845, test=0.560) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.835, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.848, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.852, test=0.557) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.844, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.834, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.845, test=0.551) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.842, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.856, test=0.575) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.844, test=0.560) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.845, test=0.560) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.835, test=0.598) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.848, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.852, test=0.557) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.844, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.834, test=0.578) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.845, test=0.551) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.856, test=0.575) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.844, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.845, test=0.560) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.835, test=0.598) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.847, test=0.587) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.851, test=0.557) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.844, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.834, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.845, test=0.551) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.855, test=0.576) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.843, test=0.560) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.845, test=0.560) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.835, test=0.598) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.847, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.851, test=0.557) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.843, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.834, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.844, test=0.551) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.841, test=0.573) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.854, test=0.574) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.843, test=0.560) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.844, test=0.559) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.835, test=0.598) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.847, test=0.587) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.851, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.843, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.834, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.844, test=0.551) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.853, test=0.575) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.843, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.842, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.834, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.846, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.851, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.843, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.833, test=0.578) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.844, test=0.552) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.852, test=0.576) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.843, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.840, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.833, test=0.599) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.845, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.851, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.843, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.833, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.844, test=0.552) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.840, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.852, test=0.577) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.841, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.835, test=0.562) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.832, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.845, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.850, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.843, test=0.577) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.833, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.842, test=0.552) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.840, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.852, test=0.577) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.840, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.835, test=0.561) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.832, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.845, test=0.588) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.850, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.843, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.833, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.841, test=0.552) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.840, test=0.575) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.850, test=0.578) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.840, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.835, test=0.561) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.831, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.844, test=0.588) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.849, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.843, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.833, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.841, test=0.553) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.840, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.850, test=0.579) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.840, test=0.561) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.835, test=0.561) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.831, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.844, test=0.588) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.849, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.843, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.833, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.841, test=0.553) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014;, score=(train=0.840, test=0.575) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00014;, score=(train=0.850, test=0.579) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014;, score=(train=0.840, test=0.561) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014;, score=(train=0.835, test=0.561) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014;, score=(train=0.831, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014;, score=(train=0.844, test=0.588) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014;, score=(train=0.849, test=0.559) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00014;, score=(train=0.843, test=0.576) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00014;, score=(train=0.833, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014;, score=(train=0.841, test=0.553) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.840, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.850, test=0.579) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.840, test=0.561) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.834, test=0.561) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.830, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.844, test=0.589) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.849, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.842, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.833, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.840, test=0.553) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.839, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.849, test=0.579) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.839, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.834, test=0.561) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.830, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.844, test=0.589) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.849, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.842, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.832, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.839, test=0.553) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.839, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.849, test=0.581) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.839, test=0.562) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.834, test=0.561) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.830, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.844, test=0.588) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.849, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.842, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.832, test=0.578) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.839, test=0.553) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.839, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.848, test=0.583) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.837, test=0.562) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.834, test=0.561) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.830, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.842, test=0.589) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.848, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.842, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.832, test=0.578) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.839, test=0.553) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.836, test=0.576) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.845, test=0.587) total time=   0.6s
[CV 3/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.836, test=0.563) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.833, test=0.560) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.828, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.840, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.848, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.831, test=0.580) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.837, test=0.554) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.836, test=0.576) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.845, test=0.587) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.836, test=0.563) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.833, test=0.560) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.828, test=0.599) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.840, test=0.586) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.848, test=0.559) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.841, test=0.574) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.831, test=0.580) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.837, test=0.554) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.836, test=0.576) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.845, test=0.587) total time=   0.5s
[CV 3/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.835, test=0.564) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.833, test=0.560) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.828, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.839, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.847, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.830, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.836, test=0.555) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.836, test=0.576) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.845, test=0.587) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.834, test=0.565) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.833, test=0.560) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.828, test=0.599) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.839, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.847, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.830, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.836, test=0.555) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.836, test=0.576) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.845, test=0.587) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.834, test=0.565) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.833, test=0.560) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.828, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.839, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.847, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.829, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.836, test=0.555) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.836, test=0.576) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.845, test=0.587) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.834, test=0.565) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.833, test=0.560) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.828, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.839, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.847, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.829, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.836, test=0.555) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.834, test=0.576) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.845, test=0.588) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.834, test=0.565) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.833, test=0.560) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.828, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.839, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.847, test=0.559) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.829, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.833, test=0.559) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.834, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.845, test=0.588) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.834, test=0.565) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.833, test=0.561) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.828, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.837, test=0.585) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.846, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.841, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.829, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.830, test=0.562) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.833, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.843, test=0.588) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.834, test=0.565) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.832, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.826, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.837, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.846, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.840, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.827, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.830, test=0.562) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.833, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.843, test=0.588) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.833, test=0.566) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.832, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.826, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.837, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.846, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.839, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.827, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.830, test=0.562) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.833, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.843, test=0.588) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.833, test=0.566) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.832, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.825, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.836, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.845, test=0.558) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.839, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.827, test=0.579) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.830, test=0.562) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.832, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.842, test=0.588) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.832, test=0.565) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.832, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.825, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.836, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.845, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.838, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.827, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.830, test=0.562) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.832, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.842, test=0.588) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.831, test=0.565) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.831, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.825, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.836, test=0.588) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.845, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.837, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.827, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.829, test=0.561) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.832, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.841, test=0.587) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.831, test=0.565) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.831, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.825, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.836, test=0.588) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.845, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.837, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.827, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.828, test=0.562) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.830, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.841, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.830, test=0.565) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.831, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.825, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.834, test=0.590) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.844, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.836, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.827, test=0.579) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.828, test=0.563) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.830, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.840, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.830, test=0.565) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.831, test=0.564) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.825, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.832, test=0.587) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.844, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.836, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.826, test=0.580) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.827, test=0.563) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.830, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.840, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.830, test=0.565) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.831, test=0.564) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.825, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.832, test=0.588) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.844, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.836, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.826, test=0.580) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.826, test=0.567) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.830, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.840, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.829, test=0.564) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.831, test=0.564) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.825, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.832, test=0.588) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.844, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.836, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.826, test=0.580) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.826, test=0.567) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.830, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.840, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.829, test=0.564) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.830, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.824, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.831, test=0.587) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.844, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.835, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.826, test=0.580) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.825, test=0.567) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.830, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.840, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.827, test=0.564) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.830, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.824, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.831, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.844, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.835, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.826, test=0.580) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.825, test=0.567) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.829, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.840, test=0.586) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.827, test=0.566) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.830, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.823, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.830, test=0.585) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.842, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.833, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.826, test=0.580) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.825, test=0.567) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.829, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.840, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.827, test=0.566) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.830, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.823, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.830, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.842, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.833, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.826, test=0.580) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.825, test=0.567) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.827, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.838, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.829, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.823, test=0.600) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.829, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.841, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.833, test=0.575) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.823, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.823, test=0.568) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.827, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.838, test=0.586) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.829, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.823, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.829, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.841, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.832, test=0.575) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.822, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.823, test=0.568) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.826, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.838, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.829, test=0.562) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.823, test=0.600) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.827, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.841, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.830, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.822, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.823, test=0.568) total time=   0.3s
[CV 1/10] END ccp_alpha=0.000144701436130007;, score=(train=0.826, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.000144701436130007;, score=(train=0.838, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.000144701436130007;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.000144701436130007;, score=(train=0.829, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.000144701436130007;, score=(train=0.823, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000144701436130007;, score=(train=0.827, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000144701436130007;, score=(train=0.841, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000144701436130007;, score=(train=0.830, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.000144701436130007;, score=(train=0.822, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000144701436130007;, score=(train=0.823, test=0.568) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.826, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.838, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.829, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.823, test=0.600) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.827, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.841, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.830, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.822, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.823, test=0.568) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.826, test=0.574) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.838, test=0.586) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.829, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.823, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.827, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.841, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.830, test=0.573) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.822, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.823, test=0.568) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.824, test=0.573) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.838, test=0.587) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.828, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.821, test=0.599) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.826, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.839, test=0.560) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.829, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.820, test=0.580) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.823, test=0.568) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.824, test=0.573) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.837, test=0.589) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.827, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.821, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.825, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.838, test=0.560) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.828, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.819, test=0.581) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.822, test=0.568) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.824, test=0.573) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.836, test=0.588) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.825, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.821, test=0.599) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.824, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.838, test=0.560) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.828, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.819, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.821, test=0.566) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.823, test=0.575) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.835, test=0.588) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.825, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.821, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.824, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.838, test=0.560) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.828, test=0.574) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.819, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.821, test=0.566) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.823, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.833, test=0.591) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.823, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.824, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.819, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.823, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.838, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.827, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.819, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.820, test=0.566) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.822, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.830, test=0.592) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.822, test=0.566) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.824, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.818, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.823, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.838, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.827, test=0.573) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.819, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.820, test=0.566) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.819, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.830, test=0.592) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.822, test=0.566) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.824, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.817, test=0.603) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.823, test=0.587) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.838, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.826, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.819, test=0.582) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.820, test=0.565) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.819, test=0.575) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.830, test=0.592) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.822, test=0.566) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.824, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.817, test=0.603) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.821, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.838, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.826, test=0.573) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.817, test=0.582) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.819, test=0.566) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.819, test=0.575) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.830, test=0.592) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.821, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.824, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.817, test=0.601) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.821, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.838, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.826, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.817, test=0.581) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.819, test=0.566) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.819, test=0.575) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.830, test=0.592) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.821, test=0.567) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.824, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.817, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.821, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.838, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.826, test=0.573) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.816, test=0.582) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.819, test=0.566) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.818, test=0.576) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.829, test=0.592) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.819, test=0.571) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.824, test=0.563) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.815, test=0.599) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.820, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.838, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.826, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.814, test=0.584) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.819, test=0.566) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.817, test=0.576) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.829, test=0.592) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.818, test=0.571) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.824, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.814, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.819, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.837, test=0.561) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.826, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.814, test=0.584) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.817, test=0.567) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.817, test=0.576) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.829, test=0.592) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.818, test=0.570) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.824, test=0.563) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.814, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.819, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.837, test=0.561) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.826, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.814, test=0.584) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.817, test=0.567) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.815, test=0.580) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.822, test=0.594) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.817, test=0.571) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.822, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.812, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.816, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.834, test=0.560) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.822, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.814, test=0.585) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.813, test=0.570) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.815, test=0.580) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.822, test=0.594) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.817, test=0.571) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.822, test=0.563) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.811, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.816, test=0.584) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.834, test=0.560) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.822, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.814, test=0.585) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.813, test=0.570) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.814, test=0.580) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.822, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.813, test=0.572) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.820, test=0.562) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.811, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.815, test=0.585) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.833, test=0.560) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.822, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.814, test=0.585) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.813, test=0.570) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.814, test=0.580) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.822, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.813, test=0.572) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.820, test=0.563) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.811, test=0.603) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.814, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.832, test=0.558) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.821, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.814, test=0.585) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.812, test=0.570) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.812, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.821, test=0.596) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.813, test=0.571) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.818, test=0.565) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.811, test=0.603) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.814, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.828, test=0.559) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.820, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.813, test=0.587) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.812, test=0.570) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.812, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.821, test=0.596) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.813, test=0.571) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.818, test=0.565) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.811, test=0.603) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.814, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.828, test=0.559) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.820, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.813, test=0.587) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.812, test=0.570) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.812, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.821, test=0.596) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.813, test=0.571) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.818, test=0.565) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.810, test=0.603) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.814, test=0.583) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.828, test=0.559) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.820, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.813, test=0.587) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.812, test=0.570) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.811, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.820, test=0.596) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.809, test=0.575) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.818, test=0.565) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.808, test=0.603) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.812, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.827, test=0.560) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.818, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.813, test=0.587) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.812, test=0.570) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.811, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.820, test=0.596) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.809, test=0.575) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.817, test=0.566) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.808, test=0.603) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.812, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.827, test=0.560) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.818, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.813, test=0.587) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.812, test=0.570) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.810, test=0.578) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.818, test=0.594) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.807, test=0.576) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.817, test=0.565) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.806, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.811, test=0.586) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.826, test=0.560) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.816, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.810, test=0.587) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.810, test=0.572) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.807, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.814, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.805, test=0.576) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.812, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.803, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.807, test=0.589) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.819, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.815, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.803, test=0.596) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.799, test=0.577) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.807, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.814, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.805, test=0.576) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.812, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.803, test=0.600) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.807, test=0.589) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.819, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.815, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.803, test=0.596) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.799, test=0.577) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.805, test=0.580) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.812, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.804, test=0.576) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.812, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.801, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.804, test=0.587) total time=   0.5s
[CV 7/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.818, test=0.562) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.815, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.802, test=0.597) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.798, test=0.576) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.805, test=0.580) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.811, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.803, test=0.576) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.812, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.801, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.803, test=0.588) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.818, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.814, test=0.577) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.802, test=0.597) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.798, test=0.576) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.805, test=0.580) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.810, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.802, test=0.576) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.810, test=0.566) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.800, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.800, test=0.590) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.817, test=0.563) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.814, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.801, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.798, test=0.577) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.805, test=0.580) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.809, test=0.594) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.802, test=0.576) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.810, test=0.566) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.800, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.800, test=0.590) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.817, test=0.563) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.814, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.801, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.797, test=0.578) total time=   0.3s
[CV 1/10] END ccp_alpha=0.000152562358276644;, score=(train=0.804, test=0.580) total time=   0.3s
[CV 2/10] END ccp_alpha=0.000152562358276644;, score=(train=0.809, test=0.594) total time=   0.3s
[CV 3/10] END ccp_alpha=0.000152562358276644;, score=(train=0.802, test=0.576) total time=   0.3s
[CV 4/10] END ccp_alpha=0.000152562358276644;, score=(train=0.810, test=0.566) total time=   0.4s
[CV 5/10] END ccp_alpha=0.000152562358276644;, score=(train=0.800, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000152562358276644;, score=(train=0.799, test=0.590) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000152562358276644;, score=(train=0.817, test=0.563) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000152562358276644;, score=(train=0.814, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.000152562358276644;, score=(train=0.801, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000152562358276644;, score=(train=0.797, test=0.578) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.799, test=0.579) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.803, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.802, test=0.575) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.810, test=0.566) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.800, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.799, test=0.590) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.817, test=0.563) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.814, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.801, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.796, test=0.578) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.799, test=0.579) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.803, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.802, test=0.575) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.810, test=0.566) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.800, test=0.602) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.799, test=0.590) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.817, test=0.562) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.814, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.801, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.795, test=0.578) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.798, test=0.580) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.802, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.801, test=0.576) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.810, test=0.566) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.794, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.799, test=0.590) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.814, test=0.562) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.814, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.801, test=0.600) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.795, test=0.579) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.797, test=0.582) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.802, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.793, test=0.577) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.809, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.794, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.799, test=0.590) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.812, test=0.565) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.809, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.794, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.795, test=0.579) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.797, test=0.582) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.802, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.793, test=0.577) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.809, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.794, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.799, test=0.590) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.812, test=0.565) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.809, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.794, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.795, test=0.579) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.797, test=0.582) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.801, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.793, test=0.577) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.809, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.794, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.799, test=0.590) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.812, test=0.565) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.809, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.794, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.795, test=0.579) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.797, test=0.582) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.801, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.793, test=0.577) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.809, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.794, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.799, test=0.590) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.812, test=0.565) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.809, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.794, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.795, test=0.579) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.797, test=0.582) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.801, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.793, test=0.577) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.809, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.793, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.799, test=0.590) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.811, test=0.565) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.809, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.794, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.795, test=0.579) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.797, test=0.582) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.801, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.793, test=0.577) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.809, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.793, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.799, test=0.590) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.811, test=0.565) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.809, test=0.572) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.794, test=0.599) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.795, test=0.579) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.796, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.801, test=0.594) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.793, test=0.577) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.809, test=0.567) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.793, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.793, test=0.592) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.811, test=0.565) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.808, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.794, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.795, test=0.579) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.796, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.800, test=0.594) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.793, test=0.577) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.808, test=0.569) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.793, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.793, test=0.592) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.811, test=0.565) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.808, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.793, test=0.597) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.795, test=0.579) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.796, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.797, test=0.594) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.792, test=0.577) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.807, test=0.571) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.792, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.793, test=0.593) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.811, test=0.565) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.808, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.793, test=0.597) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.791, test=0.581) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.796, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.797, test=0.593) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.792, test=0.577) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.807, test=0.571) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.792, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.793, test=0.593) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.810, test=0.565) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.808, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.789, test=0.600) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.791, test=0.581) total time=   0.3s
[CV 1/10] END ccp_alpha=0.000154681389463998;, score=(train=0.796, test=0.581) total time=   0.3s
[CV 2/10] END ccp_alpha=0.000154681389463998;, score=(train=0.797, test=0.593) total time=   0.4s
[CV 3/10] END ccp_alpha=0.000154681389463998;, score=(train=0.789, test=0.581) total time=   0.4s
[CV 4/10] END ccp_alpha=0.000154681389463998;, score=(train=0.807, test=0.571) total time=   0.3s
[CV 5/10] END ccp_alpha=0.000154681389463998;, score=(train=0.792, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000154681389463998;, score=(train=0.793, test=0.593) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000154681389463998;, score=(train=0.810, test=0.566) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000154681389463998;, score=(train=0.808, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.000154681389463998;, score=(train=0.789, test=0.600) total time=   0.4s
[CV 10/10] END ccp_alpha=0.000154681389463998;, score=(train=0.791, test=0.581) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.794, test=0.583) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.795, test=0.593) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.787, test=0.582) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.807, test=0.571) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.791, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.792, test=0.593) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.810, test=0.566) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.808, test=0.574) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.789, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.790, test=0.582) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.793, test=0.583) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.795, test=0.594) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.786, test=0.580) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.807, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.790, test=0.600) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.792, test=0.593) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.809, test=0.568) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.808, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.789, test=0.599) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.788, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.793, test=0.583) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.795, test=0.594) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.785, test=0.581) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.807, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.790, test=0.600) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.792, test=0.593) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.809, test=0.568) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.808, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.789, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.788, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.793, test=0.583) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.794, test=0.594) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.784, test=0.583) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.806, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.790, test=0.600) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.790, test=0.594) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.807, test=0.567) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.803, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.788, test=0.599) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.788, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.793, test=0.583) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.794, test=0.594) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.784, test=0.583) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.806, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.789, test=0.599) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.789, test=0.595) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.807, test=0.567) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.803, test=0.573) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.788, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.787, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.000155809896015074;, score=(train=0.793, test=0.583) total time=   0.4s
[CV 2/10] END ccp_alpha=0.000155809896015074;, score=(train=0.794, test=0.594) total time=   0.4s
[CV 3/10] END ccp_alpha=0.000155809896015074;, score=(train=0.784, test=0.583) total time=   0.3s
[CV 4/10] END ccp_alpha=0.000155809896015074;, score=(train=0.806, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.000155809896015074;, score=(train=0.789, test=0.598) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000155809896015074;, score=(train=0.789, test=0.595) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000155809896015074;, score=(train=0.807, test=0.567) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000155809896015074;, score=(train=0.803, test=0.573) total time=   0.4s
[CV 9/10] END ccp_alpha=0.000155809896015074;, score=(train=0.788, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000155809896015074;, score=(train=0.787, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.791, test=0.583) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.794, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.783, test=0.583) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.806, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.788, test=0.598) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.789, test=0.595) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.806, test=0.567) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.803, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.787, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.787, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.791, test=0.583) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.794, test=0.595) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.783, test=0.583) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.806, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.788, test=0.598) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.788, test=0.595) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.806, test=0.567) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.803, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.787, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.787, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.791, test=0.583) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.794, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.783, test=0.583) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.806, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.788, test=0.598) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.788, test=0.595) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.806, test=0.567) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.803, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.787, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.787, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.791, test=0.583) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.792, test=0.597) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.783, test=0.584) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.804, test=0.573) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.788, test=0.598) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.788, test=0.596) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.805, test=0.568) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.803, test=0.574) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.786, test=0.600) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.786, test=0.579) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.791, test=0.583) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.792, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.783, test=0.584) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.804, test=0.573) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.788, test=0.598) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.787, test=0.595) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.801, test=0.569) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.803, test=0.574) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.786, test=0.600) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.782, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.790, test=0.585) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.792, test=0.598) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.783, test=0.585) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.804, test=0.573) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.783, test=0.597) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.786, test=0.596) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.801, test=0.570) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.801, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.785, test=0.600) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.782, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.785, test=0.583) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.786, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.781, test=0.585) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.802, test=0.574) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.781, test=0.601) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.783, test=0.597) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.799, test=0.570) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.799, test=0.576) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.779, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.779, test=0.583) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.783, test=0.582) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.778, test=0.597) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.775, test=0.585) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.801, test=0.573) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.776, test=0.604) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.781, test=0.598) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.795, test=0.573) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.795, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.779, test=0.599) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.777, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.783, test=0.582) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.776, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.775, test=0.585) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.799, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.775, test=0.604) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.780, test=0.598) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.794, test=0.574) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.795, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.779, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.776, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.779, test=0.584) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.776, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.771, test=0.588) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.796, test=0.566) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.775, test=0.604) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.776, test=0.599) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.794, test=0.574) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.795, test=0.579) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.779, test=0.599) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.776, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.779, test=0.584) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.776, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.771, test=0.588) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.795, test=0.569) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.774, test=0.605) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.776, test=0.600) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.794, test=0.573) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.791, test=0.580) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.779, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.775, test=0.580) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.779, test=0.584) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.775, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.770, test=0.589) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.795, test=0.569) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.774, test=0.605) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.776, test=0.600) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.794, test=0.573) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.790, test=0.577) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.777, test=0.600) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.772, test=0.583) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.779, test=0.584) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.775, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.770, test=0.589) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.795, test=0.569) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.773, test=0.607) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.776, test=0.600) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.792, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.789, test=0.579) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.777, test=0.600) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.772, test=0.583) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.779, test=0.584) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.775, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.770, test=0.589) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.795, test=0.569) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.773, test=0.607) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.776, test=0.600) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.792, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.789, test=0.579) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.777, test=0.600) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.772, test=0.583) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.779, test=0.584) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.775, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.770, test=0.589) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.795, test=0.569) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.773, test=0.607) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.776, test=0.600) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.792, test=0.574) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.789, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.775, test=0.601) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.772, test=0.583) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.775, test=0.584) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.775, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.769, test=0.589) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.795, test=0.569) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.773, test=0.608) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.775, test=0.601) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.792, test=0.574) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.789, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.775, test=0.601) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.771, test=0.582) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.773, test=0.586) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.773, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.769, test=0.589) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.793, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.773, test=0.608) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.774, test=0.600) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.790, test=0.573) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.788, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.774, test=0.600) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.770, test=0.583) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.773, test=0.586) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.773, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.768, test=0.589) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.792, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.773, test=0.608) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.773, test=0.600) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.790, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.788, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.772, test=0.600) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.769, test=0.583) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.773, test=0.586) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.773, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.768, test=0.589) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.792, test=0.570) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.773, test=0.608) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.773, test=0.600) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.790, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.788, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.772, test=0.600) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.768, test=0.583) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.772, test=0.587) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.771, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.767, test=0.588) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.789, test=0.574) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.772, test=0.608) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.772, test=0.601) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.789, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.787, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.771, test=0.601) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.763, test=0.584) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.772, test=0.587) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.771, test=0.599) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.767, test=0.588) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.789, test=0.574) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.772, test=0.608) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.771, test=0.601) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.789, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.787, test=0.578) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.771, test=0.601) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.763, test=0.584) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.771, test=0.587) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.766, test=0.598) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.764, test=0.585) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.786, test=0.576) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.771, test=0.607) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.770, test=0.603) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.784, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.779, test=0.581) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.770, test=0.600) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.762, test=0.587) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.771, test=0.586) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.765, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.764, test=0.585) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.785, test=0.576) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.770, test=0.608) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.768, test=0.603) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.784, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.778, test=0.580) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.769, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.762, test=0.587) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.771, test=0.586) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.765, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.764, test=0.585) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.785, test=0.576) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.770, test=0.608) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.768, test=0.603) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.784, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.778, test=0.580) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.769, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.762, test=0.587) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.770, test=0.586) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.764, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.763, test=0.585) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.784, test=0.575) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.770, test=0.608) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.767, test=0.605) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.781, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.778, test=0.580) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.769, test=0.599) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.761, test=0.590) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.770, test=0.586) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.764, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.763, test=0.585) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.783, test=0.577) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.770, test=0.610) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.767, test=0.605) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.781, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.778, test=0.581) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.769, test=0.599) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.761, test=0.590) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.770, test=0.586) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.762, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.763, test=0.585) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.782, test=0.575) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.769, test=0.609) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.766, test=0.603) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.781, test=0.576) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.776, test=0.584) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.767, test=0.600) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.761, test=0.591) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.769, test=0.587) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.760, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.761, test=0.585) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.773, test=0.580) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.766, test=0.610) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.765, test=0.602) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.780, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.770, test=0.585) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.765, test=0.605) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.760, test=0.591) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.769, test=0.587) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.759, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.761, test=0.585) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.773, test=0.580) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.766, test=0.610) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.765, test=0.602) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.780, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.770, test=0.585) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.765, test=0.605) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.760, test=0.591) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.767, test=0.588) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.758, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.760, test=0.584) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.771, test=0.581) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.765, test=0.608) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.765, test=0.602) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.780, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.769, test=0.585) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.761, test=0.603) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.760, test=0.590) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.761, test=0.591) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.755, test=0.602) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.760, test=0.583) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.771, test=0.580) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.764, test=0.608) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.763, test=0.603) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.779, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.768, test=0.587) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.760, test=0.603) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.760, test=0.590) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.761, test=0.591) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.755, test=0.602) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.760, test=0.583) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.771, test=0.580) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.764, test=0.608) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.763, test=0.603) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.779, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.768, test=0.587) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.760, test=0.603) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.759, test=0.589) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.761, test=0.591) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.753, test=0.603) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.760, test=0.583) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.770, test=0.579) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.763, test=0.609) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.763, test=0.603) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.779, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.768, test=0.587) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.759, test=0.606) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.759, test=0.589) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.761, test=0.591) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.753, test=0.603) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.760, test=0.583) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.770, test=0.579) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.763, test=0.609) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.763, test=0.603) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.779, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.768, test=0.587) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.759, test=0.606) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.759, test=0.589) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.761, test=0.591) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.753, test=0.603) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.760, test=0.583) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.770, test=0.579) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.762, test=0.609) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.763, test=0.603) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.779, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.768, test=0.588) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.759, test=0.606) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.759, test=0.589) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.759, test=0.591) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.753, test=0.603) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.760, test=0.583) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.770, test=0.579) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.762, test=0.609) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.763, test=0.603) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.779, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.768, test=0.588) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.757, test=0.604) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.759, test=0.589) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.759, test=0.591) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.753, test=0.603) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.759, test=0.584) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.770, test=0.579) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.762, test=0.609) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.763, test=0.603) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.779, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.768, test=0.588) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.757, test=0.604) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.759, test=0.589) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.753, test=0.594) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.748, test=0.601) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.751, test=0.594) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.761, test=0.584) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.754, test=0.606) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.756, test=0.608) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.771, test=0.574) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.758, test=0.592) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.751, test=0.610) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.755, test=0.593) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.752, test=0.592) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.748, test=0.601) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.751, test=0.594) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.761, test=0.583) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.754, test=0.606) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.756, test=0.608) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.770, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.758, test=0.592) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.751, test=0.610) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.755, test=0.593) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.752, test=0.592) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.747, test=0.600) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.751, test=0.594) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.761, test=0.583) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.754, test=0.606) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.756, test=0.608) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.769, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.758, test=0.592) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.751, test=0.610) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.755, test=0.593) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.747, test=0.591) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.747, test=0.601) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.750, test=0.593) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.757, test=0.585) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.754, test=0.606) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.755, test=0.607) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.768, test=0.579) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.758, test=0.592) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.751, test=0.610) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.754, test=0.593) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.746, test=0.593) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.747, test=0.601) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.750, test=0.593) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.757, test=0.585) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.753, test=0.607) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.755, test=0.607) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.767, test=0.579) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.758, test=0.592) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.751, test=0.610) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.754, test=0.593) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.746, test=0.593) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.747, test=0.601) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.750, test=0.593) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.757, test=0.585) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.752, test=0.609) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.755, test=0.607) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.767, test=0.578) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.758, test=0.592) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.751, test=0.610) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.754, test=0.593) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.746, test=0.593) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.747, test=0.601) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.750, test=0.593) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.757, test=0.585) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.752, test=0.609) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.755, test=0.607) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.767, test=0.578) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.758, test=0.592) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.751, test=0.610) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.753, test=0.592) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.746, test=0.593) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.747, test=0.601) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.750, test=0.593) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.757, test=0.585) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.752, test=0.609) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.755, test=0.607) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.767, test=0.578) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.758, test=0.592) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.751, test=0.610) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.753, test=0.592) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.746, test=0.593) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.746, test=0.600) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.749, test=0.592) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.755, test=0.580) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.749, test=0.610) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.755, test=0.608) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.767, test=0.578) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.758, test=0.591) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.751, test=0.610) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.752, test=0.593) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.746, test=0.593) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.746, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.749, test=0.592) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.754, test=0.580) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.748, test=0.610) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.754, test=0.608) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.767, test=0.578) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.757, test=0.590) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.751, test=0.610) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.751, test=0.593) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.746, test=0.593) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.737, test=0.597) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.748, test=0.593) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.754, test=0.580) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.747, test=0.612) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.752, test=0.608) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.762, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.754, test=0.597) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.750, test=0.610) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.750, test=0.594) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.746, test=0.593) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.737, test=0.597) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.746, test=0.596) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.754, test=0.580) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.747, test=0.612) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.752, test=0.608) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.762, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.754, test=0.597) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.749, test=0.609) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.750, test=0.594) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.737, test=0.597) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.733, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.746, test=0.596) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.754, test=0.580) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.742, test=0.617) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.750, test=0.609) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.759, test=0.576) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.753, test=0.598) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.749, test=0.609) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.745, test=0.596) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.737, test=0.597) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.733, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.746, test=0.596) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.754, test=0.580) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.742, test=0.617) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.750, test=0.609) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.757, test=0.578) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.753, test=0.599) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.749, test=0.609) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.745, test=0.596) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.731, test=0.597) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.733, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.743, test=0.599) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.751, test=0.582) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.738, test=0.620) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.749, test=0.609) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.754, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.751, test=0.598) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.748, test=0.609) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.738, test=0.599) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.731, test=0.597) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.733, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.741, test=0.598) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.748, test=0.585) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.736, test=0.620) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.748, test=0.609) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.753, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.749, test=0.599) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.748, test=0.610) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.737, test=0.599) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.731, test=0.597) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.733, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.741, test=0.598) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.748, test=0.585) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.736, test=0.620) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.748, test=0.609) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.753, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.749, test=0.599) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.747, test=0.609) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.737, test=0.599) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.731, test=0.597) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.733, test=0.600) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.740, test=0.601) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.747, test=0.585) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.736, test=0.619) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.745, test=0.614) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.752, test=0.575) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.747, test=0.600) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.747, test=0.610) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.734, test=0.601) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.731, test=0.597) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.733, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.739, test=0.600) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.747, test=0.585) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.736, test=0.619) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.745, test=0.614) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.751, test=0.577) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.747, test=0.600) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.745, test=0.612) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.734, test=0.601) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.726, test=0.604) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.730, test=0.601) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.735, test=0.604) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.746, test=0.586) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.728, test=0.620) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.741, test=0.615) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.748, test=0.580) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.741, test=0.599) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.739, test=0.615) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.731, test=0.603) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.725, test=0.606) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.728, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.734, test=0.606) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.744, test=0.587) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.728, test=0.621) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.741, test=0.615) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.748, test=0.580) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.740, test=0.600) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.738, test=0.615) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.729, test=0.603) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.720, test=0.609) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.726, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.734, test=0.606) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.743, test=0.588) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.725, test=0.623) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.740, test=0.617) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.746, test=0.580) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.736, test=0.605) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.738, test=0.615) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.729, test=0.604) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.720, test=0.609) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.726, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.731, test=0.610) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.742, test=0.589) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.724, test=0.625) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.739, test=0.617) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.745, test=0.582) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.736, test=0.605) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.737, test=0.615) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.729, test=0.604) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.720, test=0.609) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.726, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.728, test=0.611) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.741, test=0.588) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.723, test=0.624) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.739, test=0.617) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.743, test=0.582) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.735, test=0.605) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.734, test=0.616) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.728, test=0.605) total time=   0.3s
[CV 1/10] END ccp_alpha=0.000181037272316907;, score=(train=0.719, test=0.609) total time=   0.3s
[CV 2/10] END ccp_alpha=0.000181037272316907;, score=(train=0.725, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.000181037272316907;, score=(train=0.726, test=0.612) total time=   0.4s
[CV 4/10] END ccp_alpha=0.000181037272316907;, score=(train=0.741, test=0.588) total time=   0.3s
[CV 5/10] END ccp_alpha=0.000181037272316907;, score=(train=0.721, test=0.625) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000181037272316907;, score=(train=0.739, test=0.617) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000181037272316907;, score=(train=0.743, test=0.582) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000181037272316907;, score=(train=0.735, test=0.605) total time=   0.3s
[CV 9/10] END ccp_alpha=0.000181037272316907;, score=(train=0.734, test=0.616) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000181037272316907;, score=(train=0.728, test=0.605) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.719, test=0.609) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.725, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.726, test=0.612) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.740, test=0.588) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.720, test=0.624) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.739, test=0.617) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.743, test=0.582) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.734, test=0.607) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.734, test=0.616) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.728, test=0.606) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.718, test=0.611) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.723, test=0.596) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.724, test=0.613) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.739, test=0.588) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.717, test=0.624) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.736, test=0.620) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.743, test=0.581) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.734, test=0.607) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.734, test=0.618) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.728, test=0.606) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.718, test=0.612) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.723, test=0.596) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.724, test=0.613) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.737, test=0.593) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.717, test=0.624) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.735, test=0.621) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.743, test=0.581) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.731, test=0.606) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.732, test=0.619) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.726, test=0.606) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.717, test=0.611) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.723, test=0.596) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.724, test=0.613) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.731, test=0.593) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.716, test=0.628) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.735, test=0.621) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.743, test=0.581) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.731, test=0.606) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.732, test=0.618) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.726, test=0.606) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.709, test=0.613) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.716, test=0.597) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.719, test=0.613) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.716, test=0.606) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.710, test=0.629) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.726, test=0.619) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.732, test=0.591) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.724, test=0.613) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.725, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.714, test=0.607) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.709, test=0.613) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.714, test=0.600) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.719, test=0.613) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.716, test=0.606) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.709, test=0.629) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.726, test=0.620) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.732, test=0.591) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.724, test=0.613) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.725, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.713, test=0.606) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.709, test=0.613) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.714, test=0.600) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.719, test=0.613) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.716, test=0.606) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.709, test=0.629) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.726, test=0.620) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.732, test=0.591) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.724, test=0.613) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.725, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.713, test=0.606) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.708, test=0.613) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.710, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.709, test=0.614) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.714, test=0.604) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.707, test=0.626) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.726, test=0.619) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.730, test=0.593) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.720, test=0.612) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.724, test=0.619) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.710, test=0.603) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.708, test=0.613) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.710, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.709, test=0.614) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.713, test=0.604) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.707, test=0.626) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.723, test=0.619) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.730, test=0.593) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.720, test=0.612) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.724, test=0.619) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.710, test=0.603) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.708, test=0.613) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.710, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.709, test=0.614) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.713, test=0.604) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.707, test=0.626) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.723, test=0.619) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.730, test=0.593) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.720, test=0.612) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.723, test=0.619) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.710, test=0.603) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.707, test=0.614) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.710, test=0.598) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.709, test=0.615) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.713, test=0.604) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.698, test=0.629) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.723, test=0.619) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.730, test=0.593) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.719, test=0.613) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.723, test=0.619) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.710, test=0.603) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.707, test=0.614) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.710, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.709, test=0.615) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.713, test=0.604) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.698, test=0.629) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.723, test=0.619) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.729, test=0.592) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.719, test=0.613) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.723, test=0.619) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.710, test=0.603) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.706, test=0.615) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.708, test=0.598) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.707, test=0.616) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.710, test=0.605) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.696, test=0.631) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.718, test=0.621) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.728, test=0.591) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.712, test=0.613) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.722, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.709, test=0.601) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.706, test=0.615) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.708, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.707, test=0.616) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.710, test=0.605) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.696, test=0.631) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.718, test=0.621) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.728, test=0.591) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.712, test=0.613) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.722, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.708, test=0.603) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.706, test=0.615) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.707, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.706, test=0.616) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.710, test=0.606) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.693, test=0.636) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.708, test=0.625) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.726, test=0.592) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.712, test=0.613) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.722, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.708, test=0.603) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.706, test=0.615) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.707, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.706, test=0.617) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.709, test=0.607) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.692, test=0.635) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.708, test=0.625) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.726, test=0.592) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.712, test=0.612) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.722, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.708, test=0.603) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.706, test=0.615) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.707, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.706, test=0.617) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.707, test=0.609) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.690, test=0.636) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.708, test=0.625) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.726, test=0.592) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.712, test=0.612) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.721, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.708, test=0.603) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.703, test=0.616) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.706, test=0.598) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.706, test=0.617) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.707, test=0.609) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.690, test=0.636) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.708, test=0.625) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.725, test=0.593) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.712, test=0.612) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.721, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.708, test=0.603) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.703, test=0.615) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.705, test=0.599) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.702, test=0.616) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.704, test=0.609) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.689, test=0.636) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.706, test=0.627) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.724, test=0.593) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.710, test=0.613) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.719, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.707, test=0.604) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.703, test=0.615) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.700, test=0.604) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.702, test=0.616) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.703, test=0.610) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.688, test=0.637) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.703, test=0.629) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.722, test=0.594) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.708, test=0.615) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.719, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.705, test=0.605) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.699, test=0.616) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.699, test=0.604) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.702, test=0.616) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.703, test=0.610) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.687, test=0.638) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.702, test=0.629) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.719, test=0.591) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.708, test=0.615) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.718, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.704, test=0.604) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.699, test=0.616) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.697, test=0.601) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.701, test=0.618) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.703, test=0.610) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.686, test=0.638) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.698, test=0.628) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.718, test=0.591) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.708, test=0.615) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.717, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.702, test=0.607) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.699, test=0.616) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.697, test=0.601) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.701, test=0.618) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.702, test=0.611) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.686, test=0.638) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.698, test=0.628) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.718, test=0.591) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.708, test=0.616) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.716, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.702, test=0.607) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.699, test=0.616) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.696, test=0.602) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.699, test=0.619) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.695, test=0.611) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.684, test=0.640) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.693, test=0.630) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.710, test=0.599) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.706, test=0.618) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.715, test=0.621) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.696, test=0.606) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.695, test=0.622) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.695, test=0.602) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.699, test=0.618) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.695, test=0.613) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.684, test=0.640) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.693, test=0.630) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.708, test=0.599) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.706, test=0.618) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.715, test=0.621) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.696, test=0.606) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.695, test=0.622) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.695, test=0.602) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.699, test=0.618) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.695, test=0.613) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.684, test=0.640) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.693, test=0.630) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.708, test=0.599) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.706, test=0.618) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.708, test=0.622) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.696, test=0.606) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.695, test=0.622) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.693, test=0.601) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.699, test=0.618) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.695, test=0.613) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.684, test=0.640) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.693, test=0.630) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.708, test=0.599) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.704, test=0.620) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.708, test=0.622) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.696, test=0.606) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.695, test=0.622) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.693, test=0.601) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.699, test=0.618) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.695, test=0.613) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.684, test=0.640) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.693, test=0.630) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.708, test=0.599) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.704, test=0.620) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.708, test=0.622) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.696, test=0.606) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.686, test=0.626) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.684, test=0.608) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.691, test=0.621) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.688, test=0.617) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.677, test=0.646) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.684, test=0.631) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.694, test=0.600) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.689, test=0.628) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.691, test=0.628) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.680, test=0.614) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.679, test=0.630) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.682, test=0.608) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.687, test=0.623) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.675, test=0.631) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.670, test=0.643) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.674, test=0.636) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.688, test=0.603) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.680, test=0.632) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.683, test=0.633) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.680, test=0.613) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.678, test=0.629) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.675, test=0.607) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.682, test=0.625) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.669, test=0.636) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.669, test=0.643) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.674, test=0.636) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.688, test=0.603) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.677, test=0.636) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.680, test=0.630) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.672, test=0.624) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.677, test=0.628) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.674, test=0.608) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.670, test=0.637) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.667, test=0.637) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.667, test=0.643) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.672, test=0.636) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.678, test=0.609) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.670, test=0.636) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.672, test=0.639) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.671, test=0.623) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.677, test=0.628) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.674, test=0.608) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.670, test=0.637) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.667, test=0.637) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.667, test=0.643) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.671, test=0.640) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.675, test=0.609) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.670, test=0.636) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.672, test=0.639) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.671, test=0.623) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.676, test=0.630) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.674, test=0.608) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.670, test=0.637) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.667, test=0.637) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.667, test=0.643) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.671, test=0.640) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.675, test=0.610) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.669, test=0.638) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.670, test=0.639) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.670, test=0.626) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.676, test=0.630) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.672, test=0.610) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.668, test=0.640) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.666, test=0.635) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.666, test=0.642) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.670, test=0.639) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.673, test=0.607) total time=   0.4s
[CV 8/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.663, test=0.640) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.667, test=0.640) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.670, test=0.626) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.673, test=0.635) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.672, test=0.610) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.668, test=0.640) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.666, test=0.635) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.666, test=0.642) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.670, test=0.639) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.673, test=0.607) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.663, test=0.640) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.667, test=0.640) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.670, test=0.626) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.669, test=0.642) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.667, test=0.617) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.661, test=0.644) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.663, test=0.636) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.666, test=0.642) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.665, test=0.646) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.670, test=0.609) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.659, test=0.642) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.665, test=0.639) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.666, test=0.626) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.668, test=0.642) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.660, test=0.620) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.658, test=0.643) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.662, test=0.636) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.661, test=0.644) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.664, test=0.645) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.667, test=0.608) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.658, test=0.643) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.661, test=0.640) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.665, test=0.626) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.661, test=0.642) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.657, test=0.622) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.657, test=0.641) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.660, test=0.637) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.657, test=0.647) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.662, test=0.646) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.667, test=0.608) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.656, test=0.641) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.658, test=0.642) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.659, test=0.630) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.658, test=0.641) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.651, test=0.627) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.655, test=0.636) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.659, test=0.637) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.656, test=0.650) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.660, test=0.647) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.667, test=0.608) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.655, test=0.640) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.658, test=0.642) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.656, test=0.632) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.656, test=0.640) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.651, test=0.627) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.654, test=0.636) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.657, test=0.643) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.655, test=0.650) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.660, test=0.647) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.664, test=0.612) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.654, test=0.642) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.654, test=0.641) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.656, test=0.632) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.656, test=0.640) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.649, test=0.628) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.654, test=0.636) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.655, test=0.643) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.652, test=0.650) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.656, test=0.647) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.660, test=0.616) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.651, test=0.641) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.654, test=0.641) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.656, test=0.631) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.656, test=0.640) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.649, test=0.628) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.654, test=0.636) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.655, test=0.643) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.652, test=0.650) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.656, test=0.647) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.660, test=0.616) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.651, test=0.641) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.654, test=0.641) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.656, test=0.631) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.656, test=0.640) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.649, test=0.628) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.652, test=0.636) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.655, test=0.643) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.651, test=0.649) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.656, test=0.647) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.659, test=0.620) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.651, test=0.641) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.654, test=0.641) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.655, test=0.631) total time=   0.4s
[CV 1/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.654, test=0.637) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.647, test=0.625) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.652, test=0.636) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.653, test=0.644) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.651, test=0.649) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.654, test=0.647) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.659, test=0.620) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.650, test=0.638) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.654, test=0.641) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.653, test=0.634) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.654, test=0.637) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.647, test=0.625) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.652, test=0.636) total time=   0.4s
[CV 4/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.653, test=0.644) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.647, test=0.644) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.651, test=0.647) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.659, test=0.620) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.650, test=0.638) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.654, test=0.641) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.653, test=0.634) total time=   0.4s
[CV 1/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.651, test=0.635) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.644, test=0.621) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.650, test=0.638) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.651, test=0.642) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.647, test=0.644) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.646, test=0.644) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.654, test=0.617) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.648, test=0.638) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.650, test=0.644) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.646, test=0.629) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.651, test=0.635) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.644, test=0.621) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.650, test=0.638) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.649, test=0.642) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.646, test=0.640) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.645, test=0.645) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.653, test=0.617) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.648, test=0.638) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.647, test=0.643) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.646, test=0.629) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.646, test=0.632) total time=   0.4s
[CV 2/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.644, test=0.621) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.650, test=0.638) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.649, test=0.642) total time=   0.4s
[CV 5/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.646, test=0.640) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.640, test=0.641) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.648, test=0.615) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.648, test=0.638) total time=   0.4s
[CV 9/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.643, test=0.639) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.643, test=0.628) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.646, test=0.632) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.644, test=0.621) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.650, test=0.638) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.648, test=0.641) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.646, test=0.640) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.640, test=0.640) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.648, test=0.615) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.648, test=0.638) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.643, test=0.639) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.643, test=0.628) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.646, test=0.632) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.644, test=0.621) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.647, test=0.642) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.647, test=0.640) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.646, test=0.640) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.640, test=0.640) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.648, test=0.615) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.648, test=0.638) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.641, test=0.639) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.643, test=0.628) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.646, test=0.632) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.644, test=0.621) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.647, test=0.642) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.647, test=0.640) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.646, test=0.640) total time=   0.4s
[CV 6/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.640, test=0.640) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.648, test=0.615) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.648, test=0.638) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.641, test=0.639) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.643, test=0.628) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.641, test=0.630) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.641, test=0.619) total time=   0.4s
[CV 3/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.645, test=0.640) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.642, test=0.639) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.641, test=0.639) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.636, test=0.643) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.645, test=0.614) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.645, test=0.638) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.641, test=0.639) total time=   0.4s
[CV 10/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.642, test=0.624) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.641, test=0.630) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.641, test=0.618) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.645, test=0.640) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.642, test=0.639) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.640, test=0.638) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.633, test=0.639) total time=   0.4s
[CV 7/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.645, test=0.614) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.644, test=0.636) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.641, test=0.639) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.642, test=0.624) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.638, test=0.629) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.638, test=0.616) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.634, test=0.642) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.638, test=0.637) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.639, test=0.642) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.630, test=0.638) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.638, test=0.611) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.642, test=0.636) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.640, test=0.639) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.638, test=0.625) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.638, test=0.629) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.638, test=0.616) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.634, test=0.641) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.638, test=0.637) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.639, test=0.642) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.630, test=0.637) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.638, test=0.611) total time=   0.4s
[CV 8/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.638, test=0.637) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.635, test=0.636) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.634, test=0.620) total time=   0.3s
[CV 1/10] END ccp_alpha=0.000688561405553012;, score=(train=0.628, test=0.619) total time=   0.3s
[CV 2/10] END ccp_alpha=0.000688561405553012;, score=(train=0.636, test=0.612) total time=   0.3s
[CV 3/10] END ccp_alpha=0.000688561405553012;, score=(train=0.634, test=0.641) total time=   0.3s
[CV 4/10] END ccp_alpha=0.000688561405553012;, score=(train=0.637, test=0.638) total time=   0.4s
[CV 5/10] END ccp_alpha=0.000688561405553012;, score=(train=0.636, test=0.642) total time=   0.3s
[CV 6/10] END ccp_alpha=0.000688561405553012;, score=(train=0.630, test=0.637) total time=   0.3s
[CV 7/10] END ccp_alpha=0.000688561405553012;, score=(train=0.637, test=0.613) total time=   0.3s
[CV 8/10] END ccp_alpha=0.000688561405553012;, score=(train=0.635, test=0.631) total time=   0.3s
[CV 9/10] END ccp_alpha=0.000688561405553012;, score=(train=0.635, test=0.636) total time=   0.3s
[CV 10/10] END ccp_alpha=0.000688561405553012;, score=(train=0.633, test=0.623) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.626, test=0.616) total time=   0.4s
[CV 2/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.632, test=0.610) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.630, test=0.639) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.626, test=0.629) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.629, test=0.633) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.625, test=0.636) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.633, test=0.610) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.632, test=0.629) total time=   0.4s
[CV 9/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.631, test=0.633) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.632, test=0.624) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.625, test=0.615) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.616, test=0.595) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.613, test=0.622) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.617, test=0.616) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.615, test=0.616) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.624, test=0.634) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.625, test=0.610) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.614, test=0.610) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.626, test=0.630) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.627, test=0.622) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.625, test=0.615) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.616, test=0.595) total time=   0.4s
[CV 3/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.613, test=0.622) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.612, test=0.609) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.615, test=0.616) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.624, test=0.634) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.625, test=0.610) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.614, test=0.610) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.626, test=0.630) total time=   0.4s
[CV 10/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.627, test=0.622) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.614, test=0.614) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.603, test=0.577) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.601, test=0.599) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.612, test=0.609) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.615, test=0.616) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.601, test=0.614) total time=   0.4s
[CV 7/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.615, test=0.604) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.614, test=0.610) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.615, test=0.619) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.602, test=0.603) total time=   0.3s
[CV 1/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.600, test=0.606) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.597, test=0.571) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.595, test=0.593) total time=   0.4s
[CV 4/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.595, test=0.593) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.602, test=0.604) total time=   0.3s
[CV 6/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.601, test=0.614) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.602, test=0.593) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.601, test=0.599) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.602, test=0.610) total time=   0.3s
[CV 10/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.602, test=0.603) total time=   0.4s
[CV 1/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time=   0.3s
[CV 2/10] END ccp_alpha=0.019928402011802793;, score=(train=0.597, test=0.571) total time=   0.3s
[CV 3/10] END ccp_alpha=0.019928402011802793;, score=(train=0.595, test=0.593) total time=   0.3s
[CV 4/10] END ccp_alpha=0.019928402011802793;, score=(train=0.595, test=0.593) total time=   0.3s
[CV 5/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time=   0.3s
[CV 6/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time=   0.3s
[CV 7/10] END ccp_alpha=0.019928402011802793;, score=(train=0.595, test=0.589) total time=   0.4s
[CV 8/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time=   0.3s
[CV 9/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time=   0.3s
[CV 10/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time=   0.3s
Out[114]:
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'ccp_alpha': array([0.00000000e+00, 2.74285714e-05, 3.17460317e-05, ...,
       2.68132856e-03, 3.20356827e-03, 1.99284020e-02])},
             return_train_score=True, scoring='roc_auc', verbose=4)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'ccp_alpha': array([0.00000000e+00, 2.74285714e-05, 3.17460317e-05, ...,
       2.68132856e-03, 3.20356827e-03, 1.99284020e-02])},
             return_train_score=True, scoring='roc_auc', verbose=4)
DecisionTreeClassifier(ccp_alpha=0.0003009243945642226, random_state=42)
DecisionTreeClassifier(ccp_alpha=0.0003009243945642226, random_state=42)
In [115]:
report_GridSearchCV_results(grid_search_post_prune)

- Best combination of hyperparameters:
 {'ccp_alpha': 0.0003009243945642226} 

- Best mean_test_score:
 0.6375618358423202 

- Score by fold for best estimator:
 [0.6397542997542998, 0.6280936936936937, 0.6358545454545455, 0.6431108927108927, 0.649273054873055, 0.6468022932022932, 0.6197610400092239, 0.6412566952821189, 0.6407404641363479, 0.6309713793067303] 

- Top 10 hyperparameter combinations by mean_test_score:
mean_test_score param_ccp_alpha
rank_test_score
1 0.637562 0.000301
2 0.637359 0.000294
2 0.637359 0.000296
4 0.637018 0.000286
5 0.636972 0.000322
6 0.636471 0.000325
7 0.636019 0.000279
8 0.635712 0.000267
9 0.635158 0.000346
10 0.634747 0.000254
In [116]:
compare_performance(grid_search_post_prune)
Out[116]:
train_AUC val_AUC
1 1.0 0.537137
2 1.0 0.537137
3 1.0 0.537111
4 1.0 0.537111
5 1.0 0.537111
6 1.0 0.537139
7 1.0 0.537139
8 1.0 0.537139
9 1.0 0.537139
10 1.0 0.537139
Mean 1.0 0.537131
In [117]:
best_model_post_prune=grid_search_post_prune.best_estimator_
In [124]:
plot_feature_importance_chart(best_model_post_prune, X_train, y_train, cv, "Post-pruned Classification Tree")
No description has been provided for this image
In [125]:
# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_post_prune, filled=True, feature_names=X_train.columns, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()
No description has been provided for this image
In [126]:
evaluate_model(best_model_post_prune, X_test, y_test)

Test AUC: 0.64
Accuracy: 0.61
Confusion Matrix:
[[2627 1373]
 [1546 1954]]
No description has been provided for this image 
Classification Report:
              precision    recall  f1-score   support

           0       0.63      0.66      0.64      4000
           1       0.59      0.56      0.57      3500

    accuracy                           0.61      7500
   macro avg       0.61      0.61      0.61      7500
weighted avg       0.61      0.61      0.61      7500
In [127]:
plot_roc_curve(best_model_post_prune, X_test, y_test)
No description has been provided for this image

Random Forest¶

In [133]:
# Initialize model
randomforest = RandomForestClassifier(max_depth = 6, random_state = 42, bootstrap=True)

# Define the hyperparameter grid
rf_param_grid = {
    'max_depth': [2, 3, 4],
    'min_samples_leaf': [500, 1000, 2000],
    'max_features': [2, 3], 
}

# Create a GridSearchCV object
grid_search_rf = GridSearchCV(estimator=randomforest, param_grid=rf_param_grid, cv=cv, scoring='roc_auc', verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_rf.fit(X_train, y_train)

Fitting 10 folds for each of 18 candidates, totalling 180 fits
[CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.634) total time=   0.5s
[CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.641, test=0.618) total time=   0.5s
[CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.642) total time=   0.6s
[CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.631) total time=   0.6s
[CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.644) total time=   0.6s
[CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.638, test=0.665) total time=   0.6s
[CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.610) total time=   0.5s
[CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.641, test=0.631) total time=   0.5s
[CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.642) total time=   0.6s
[CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.629) total time=   0.5s
[CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.632, test=0.626) total time=   0.3s
[CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.633, test=0.604) total time=   0.5s
[CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.633, test=0.636) total time=   0.5s
[CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.631, test=0.627) total time=   0.4s
[CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.635, test=0.641) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.631, test=0.663) total time=   0.6s
[CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.632, test=0.599) total time=   0.6s
[CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.631, test=0.629) total time=   0.5s
[CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.630, test=0.632) total time=   0.5s
[CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.632, test=0.623) total time=   0.5s
[CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.605, test=0.602) total time=   0.5s
[CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.574) total time=   0.5s
[CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.602, test=0.607) total time=   0.4s
[CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.598) total time=   0.4s
[CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.619) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.601, test=0.630) total time=   0.5s
[CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.573) total time=   0.5s
[CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.611) total time=   0.5s
[CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.597) total time=   0.4s
[CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.586) total time=   0.6s
[CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.640, test=0.636) total time=   0.5s
[CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.618) total time=   0.7s
[CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.642, test=0.645) total time=   0.8s
[CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.640, test=0.630) total time=   0.7s
[CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.640, test=0.646) total time=   0.7s
[CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.638, test=0.662) total time=   0.7s
[CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.612) total time=   0.6s
[CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.642, test=0.636) total time=   0.5s
[CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.639, test=0.649) total time=   0.5s
[CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.642, test=0.633) total time=   0.4s
[CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.635, test=0.627) total time=   0.6s
[CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.613) total time=   0.5s
[CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.637, test=0.643) total time=   0.4s
[CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.636, test=0.631) total time=   0.4s
[CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.634, test=0.643) total time=   0.5s
[CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.632, test=0.659) total time=   0.5s
[CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.640, test=0.604) total time=   0.4s
[CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.637, test=0.634) total time=   0.5s
[CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.634, test=0.642) total time=   0.5s
[CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.634, test=0.623) total time=   0.4s
[CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.612) total time=   0.4s
[CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.597) total time=   0.4s
[CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.618, test=0.620) total time=   0.5s
[CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.612) total time=   0.5s
[CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.611, test=0.628) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.610, test=0.634) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.582) total time=   0.4s
[CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.611, test=0.614) total time=   0.4s
[CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.616, test=0.617) total time=   0.4s
[CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time=   0.5s
[CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.638) total time=   0.5s
[CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.621) total time=   0.4s
[CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.644) total time=   0.5s
[CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.636) total time=   0.5s
[CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.649) total time=   0.4s
[CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.666) total time=   0.6s
[CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.613) total time=   0.6s
[CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.637) total time=   0.6s
[CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.648) total time=   0.5s
[CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.636) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.633) total time=   0.4s
[CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.611) total time=   0.4s
[CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.640, test=0.638) total time=   0.4s
[CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.633) total time=   0.4s
[CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.647) total time=   0.4s
[CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.636, test=0.664) total time=   0.4s
[CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.605) total time=   0.4s
[CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.636, test=0.633) total time=   0.4s
[CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.636, test=0.639) total time=   0.4s
[CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.629) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.605, test=0.602) total time=   0.4s
[CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.575) total time=   0.5s
[CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.602, test=0.607) total time=   0.4s
[CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.598) total time=   0.6s
[CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.618) total time=   0.5s
[CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.601, test=0.630) total time=   0.5s
[CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.572) total time=   0.3s
[CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.611) total time=   0.4s
[CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.597) total time=   0.5s
[CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.586) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.637) total time=   0.5s
[CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.619) total time=   0.5s
[CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.650) total time=   0.6s
[CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.643, test=0.633) total time=   0.5s
[CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.648) total time=   0.6s
[CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.641, test=0.663) total time=   0.5s
[CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.615) total time=   0.5s
[CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.639) total time=   0.5s
[CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.643, test=0.649) total time=   0.5s
[CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.634) total time=   0.5s
[CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.638, test=0.630) total time=   0.5s
[CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.613) total time=   0.6s
[CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.637, test=0.643) total time=   0.9s
[CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.638, test=0.631) total time=   0.8s
[CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.636, test=0.645) total time=   0.7s
[CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.633, test=0.659) total time=   0.6s
[CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.605) total time=   0.6s
[CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.636) total time=   0.7s
[CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.637, test=0.642) total time=   0.6s
[CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.637, test=0.625) total time=   0.6s
[CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.616, test=0.612) total time=   0.5s
[CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.596) total time=   0.5s
[CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.620) total time=   0.5s
[CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.611) total time=   0.6s
[CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.610, test=0.626) total time=   0.7s
[CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.610, test=0.634) total time=   0.7s
[CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.582) total time=   0.5s
[CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.611, test=0.614) total time=   0.7s
[CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.615, test=0.616) total time=   0.5s
[CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time=   0.6s
[CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.636) total time=   0.6s
[CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.621) total time=   0.5s
[CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.644) total time=   0.6s
[CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.637) total time=   0.6s
[CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.648) total time=   0.5s
[CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.669) total time=   0.5s
[CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.613) total time=   0.5s
[CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.638) total time=   0.4s
[CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.646) total time=   0.4s
[CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.635) total time=   0.5s
[CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.633) total time=   0.6s
[CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.636, test=0.609) total time=   0.5s
[CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.636) total time=   0.6s
[CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.631) total time=   0.5s
[CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.649) total time=   0.6s
[CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.635, test=0.664) total time=   0.5s
[CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.602) total time=   0.6s
[CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.635, test=0.632) total time=   0.6s
[CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.634, test=0.637) total time=   0.6s
[CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.635, test=0.627) total time=   0.5s
[CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.605, test=0.602) total time=   0.5s
[CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.575) total time=   0.4s
[CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.602, test=0.607) total time=   0.5s
[CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.598) total time=   0.8s
[CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.618) total time=   0.8s
[CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.601, test=0.630) total time=   0.8s
[CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.572) total time=   0.9s
[CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.611) total time=   0.7s
[CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.597) total time=   0.7s
[CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.586) total time=   0.6s
[CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.636) total time=   0.7s
[CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.623) total time=   0.6s
[CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.650) total time=   0.7s
[CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.633) total time=   0.8s
[CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.648) total time=   0.7s
[CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.642, test=0.664) total time=   0.7s
[CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.615) total time=   0.6s
[CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.641) total time=   0.6s
[CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.643, test=0.649) total time=   0.7s
[CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.634) total time=   0.6s
[CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.631) total time=   0.6s
[CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.614) total time=   0.6s
[CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.645) total time=   0.7s
[CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.633) total time=   0.7s
[CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.647) total time=   0.5s
[CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.636, test=0.661) total time=   0.6s
[CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.607) total time=   0.6s
[CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.640, test=0.638) total time=   0.6s
[CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.638, test=0.644) total time=   0.6s
[CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.628) total time=   0.6s
[CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.616, test=0.612) total time=   0.6s
[CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.596) total time=   0.5s
[CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.620) total time=   0.5s
[CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.611) total time=   0.9s
[CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.610, test=0.626) total time=   1.2s
[CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.610, test=0.634) total time=   1.2s
[CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.582) total time=   1.0s
[CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.611, test=0.614) total time=   1.3s
[CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.615, test=0.616) total time=   1.0s
[CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time=   0.6s
Out[133]:
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
             estimator=RandomForestClassifier(max_depth=6, random_state=42),
             param_grid={'max_depth': [2, 3, 4], 'max_features': [2, 3],
                         'min_samples_leaf': [500, 1000, 2000]},
             return_train_score=True, scoring='roc_auc', verbose=4)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
             estimator=RandomForestClassifier(max_depth=6, random_state=42),
             param_grid={'max_depth': [2, 3, 4], 'max_features': [2, 3],
                         'min_samples_leaf': [500, 1000, 2000]},
             return_train_score=True, scoring='roc_auc', verbose=4)
RandomForestClassifier(max_depth=4, max_features=3, min_samples_leaf=500,
                       random_state=42)
RandomForestClassifier(max_depth=4, max_features=3, min_samples_leaf=500,
                       random_state=42)
In [134]:
report_GridSearchCV_results(grid_search_rf)

- Best combination of hyperparameters:
 {'max_depth': 4, 'max_features': 3, 'min_samples_leaf': 500} 

- Best mean_test_score:
 0.6392741898648436 

- Score by fold for best estimator:
 [0.6364894348894349, 0.6227996723996724, 0.6501444717444718, 0.6332481572481573, 0.6477654381654381, 0.6635210483210483, 0.6147461295766381, 0.6406114063499052, 0.6493650618953283, 0.634051078058342] 

- Top 10 hyperparameter combinations by mean_test_score:
mean_test_score param_max_depth param_min_samples_leaf param_max_features
rank_test_score
1 0.639274 4 500 3
2 0.638760 3 500 2
3 0.638721 3 500 3
4 0.638607 4 500 2
5 0.636623 2 500 3
6 0.634800 4 1000 3
7 0.634666 2 500 2
8 0.633292 3 1000 2
9 0.632992 3 1000 3
10 0.631938 2 1000 3
In [135]:
compare_performance(grid_search_rf)
Out[135]:
train_AUC val_AUC
1 0.639827 0.634666
2 0.632097 0.627992
3 0.603159 0.599672
4 0.641148 0.636623
5 0.635874 0.631938
6 0.615571 0.612199
7 0.644051 0.638760
8 0.637475 0.633292
9 0.603204 0.599673
10 0.643913 0.638721
Mean 0.629632 0.625353
In [136]:
best_model_rf=grid_search_rf.best_estimator_
In [137]:
plot_feature_importance_chart(best_model_rf, X_train, y_train, cv, "Random Forest")
No description has been provided for this image
In [138]:
evaluate_model(best_model_rf, X_test, y_test)

Test AUC: 0.64
Accuracy: 0.60
Confusion Matrix:
[[3438  562]
 [2441 1059]]
No description has been provided for this image 
Classification Report:
              precision    recall  f1-score   support

           0       0.58      0.86      0.70      4000
           1       0.65      0.30      0.41      3500

    accuracy                           0.60      7500
   macro avg       0.62      0.58      0.55      7500
weighted avg       0.62      0.60      0.56      7500
In [139]:
plot_roc_curve(best_model_rf, X_test, y_test)
No description has been provided for this image

XGBoost¶

In [141]:
# Initialize model
xgb_model = xgb.XGBClassifier(random_state = 42)

# Define the hyperparameter grid
xgb_param_grid = {
    'colsample_bytree': [0.3, 0.7],
    'n_estimators': [50, 100, 200],
    'max_depth': [2, 5, 10],
    'alpha': [0, 0.1, 1], # Alpha/lasso regularisation
    'lambda': [0, 0.1, 1], # Lambda/ridge regularisation
    'learning_rate': [0.01, 0.05]    
}

# Create a GridSearchCV object
grid_search_xgb = GridSearchCV(param_grid=xgb_param_grid, estimator=xgb_model, 
                        scoring='roc_auc', cv=cv, verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_xgb.fit(X_train, y_train)

Fitting 10 folds for each of 324 candidates, totalling 3240 fits
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.655) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.651) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.647) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.652) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.6s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.654) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.669) total time=   0.7s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.640) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.661) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.672) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.629) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.651) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.653) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.640) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.660) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.662) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.673) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.689, test=0.631) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.664) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.652) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.654) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.641) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.662) total time=   0.9s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.655) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.661) total time=   0.7s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.675) total time=   1.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.697, test=0.632) total time=   1.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.652) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.665) total time=   1.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.655) total time=   1.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.647) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.635) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.658) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.647) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.654) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.668) total time=   0.7s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.834, test=0.628) total time=   0.7s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.646) total time=   0.7s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.837, test=0.652) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.645) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.648) total time=   1.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.639) total time=   0.7s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.659) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.651) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.660) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.670) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.842, test=0.631) total time=   1.6s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.645) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.660) total time=   0.7s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.647) total time=   1.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.646) total time=   1.7s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.637) total time=   1.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.659) total time=   1.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.654) total time=   1.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.657) total time=   1.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.669) total time=   1.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.631) total time=   1.6s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.646) total time=   1.5s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.872, test=0.659) total time=   1.9s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.650) total time=   1.9s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.656) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.644) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.633) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.646) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.637) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.654) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.634) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.653) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.698, test=0.644) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.664) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.652) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.676) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.698, test=0.635) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.665) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.650) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.655) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.645) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.664) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.652) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.678) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.637) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.652) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.669) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.655) total time=   0.6s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.648) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.662) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.655) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.662) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.679) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.640) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.653) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.667) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.653) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.645) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.638) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.656) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.874, test=0.639) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.649) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.672) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.876, test=0.629) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.642) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.876, test=0.645) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.640) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.640) total time=   0.6s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.637) total time=   0.7s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.654) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.639) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.648) total time=   0.7s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.673) total time=   0.7s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.629) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.638) total time=   0.7s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.650) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.638) total time=   0.7s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.629) total time=   1.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.630) total time=   1.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.650) total time=   1.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.640) total time=   1.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.646) total time=   1.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.663) total time=   1.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.628) total time=   1.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.631) total time=   1.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.652) total time=   1.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.637) total time=   1.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.651) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.648) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.652) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.654) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.670) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.640) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.672) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.628) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.653) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.652) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.640) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.660) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.673) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.689, test=0.631) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.664) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.654) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.641) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.662) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.655) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.661) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.676) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.632) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.653) total time=   0.6s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.665) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.655) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.648) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.637) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.656) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.647) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.654) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.667) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.629) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.835, test=0.654) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.645) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.649) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.639) total time=   0.7s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.659) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.650) total time=   0.7s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.660) total time=   0.7s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.670) total time=   0.7s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.632) total time=   0.7s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.645) total time=   0.7s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.843, test=0.661) total time=   0.7s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.647) total time=   0.7s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.646) total time=   1.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.637) total time=   1.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.658) total time=   1.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.653) total time=   1.6s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.657) total time=   1.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.864, test=0.669) total time=   1.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.632) total time=   1.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.647) total time=   1.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.660) total time=   1.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.864, test=0.650) total time=   1.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.644) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.634) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.646) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.637) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.654) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.652) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.654) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.698, test=0.643) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.654) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.676) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.635) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.665) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.656) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.645) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.664) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.654) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.662) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.678) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.636) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.652) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.669) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.653) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.658) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.645) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.664) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.654) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.662) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.677) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.638) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.652) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.668) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.655) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.644) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.634) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.655) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.638) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.651) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.672) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.875, test=0.628) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.640) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.648) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.641) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.640) total time=   0.6s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.633) total time=   0.7s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.654) total time=   0.6s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.636) total time=   0.7s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.903, test=0.652) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.671) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.629) total time=   0.7s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.636) total time=   0.6s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.653) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.638) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.632) total time=   1.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.630) total time=   1.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.654) total time=   1.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.636) total time=   1.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.652) total time=   1.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.662) total time=   1.5s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.626) total time=   1.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.630) total time=   1.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.648) total time=   1.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.636) total time=   1.7s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.628) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.629) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.652) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.669) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.651) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.640) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.660) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.672) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.629) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.661) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.652) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.640) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.660) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.651) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.673) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.632) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.664) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.641) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.661) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.654) total time=   1.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.661) total time=   1.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.675) total time=   1.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.632) total time=   0.9s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.653) total time=   1.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.665) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.655) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.651) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.637) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.657) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.649) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.655) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.671) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.631) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.645) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.656) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.648) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.651) total time=   1.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.640) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.659) total time=   0.9s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.651) total time=   0.9s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.661) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.671) total time=   1.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.633) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.646) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.823, test=0.662) total time=   1.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.651) total time=   0.9s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.648) total time=   1.9s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.636) total time=   2.7s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.661) total time=   2.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.654) total time=   2.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.659) total time=   2.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.670) total time=   2.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.633) total time=   1.7s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.649) total time=   1.5s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.661) total time=   1.7s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.655) total time=   2.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.642) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.628) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.649) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.634) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.649) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time=   0.6s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.636) total time=   1.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.655) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.661) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.633) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time=   0.6s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.654) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.643) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.663) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.655) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.675) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.633) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.651) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.663) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.657) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.645) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.662) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.654) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.662) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.677) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.635) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.652) total time=   0.7s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.665) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.653) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.658) total time=   0.9s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.647) total time=   1.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.664) total time=   1.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.656) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.664) total time=   0.9s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.676) total time=   1.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.637) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.651) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.666) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.658) total time=   1.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.652) total time=   0.6s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.635) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.655) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.645) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.659) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.669) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.629) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.644) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.651) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.643) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.646) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.633) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.654) total time=   0.8s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.645) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.886, test=0.657) total time=   0.7s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.669) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.631) total time=   0.9s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.641) total time=   0.9s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.891, test=0.654) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.643) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.639) total time=   1.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.630) total time=   1.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.649) total time=   1.6s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.646) total time=   1.6s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.930, test=0.652) total time=   2.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.660) total time=   1.9s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.630) total time=   1.6s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.637) total time=   1.7s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.652) total time=   1.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.642) total time=   1.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.637) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.626) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.637) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time=   0.7s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.661) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.642) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.651) total time=   0.7s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.646) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.631) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.655) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.651) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.664) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.627) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.648) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.648) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.637) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.654) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.667) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.628) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.647) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.642) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.650) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.640) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.656) total time=   0.6s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.652) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.656) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.671) total time=   0.7s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.630) total time=   0.7s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.648) total time=   0.6s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.658) total time=   0.7s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.643) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.633) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.636) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.649) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.639) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.652) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.660) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.620) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.640) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.647) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.641) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.636) total time=   0.9s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.848, test=0.633) total time=   1.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.651) total time=   0.9s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.643) total time=   0.9s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.654) total time=   1.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.664) total time=   1.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.848, test=0.621) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.637) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.852, test=0.651) total time=   0.9s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.843, test=0.643) total time=   1.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.635) total time=   1.9s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.877, test=0.639) total time=   1.9s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.873, test=0.650) total time=   1.9s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.641) total time=   1.9s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.651) total time=   1.7s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.871, test=0.666) total time=   1.9s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.876, test=0.624) total time=   1.8s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.874, test=0.636) total time=   1.7s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.877, test=0.650) total time=   1.9s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.641) total time=   1.8s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.630) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.650) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.665) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.643) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.639) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.630) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.651) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.668) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.635) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.653) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.671) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.631) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.650) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.661) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.649) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.638) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.652) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.669) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.647) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.660) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.645) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.653) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.714, test=0.643) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.657) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.650) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.658) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.672) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.716, test=0.631) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.650) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.665) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.648) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.654) total time=   0.6s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.645) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.657) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.654) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.658) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.675) total time=   0.7s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.751, test=0.635) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.646) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.665) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.649) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.881, test=0.630) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.884, test=0.631) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.889, test=0.639) total time=   0.6s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.877, test=0.636) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.881, test=0.643) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.880, test=0.654) total time=   0.9s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.886, test=0.614) total time=   0.7s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.887, test=0.629) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.886, test=0.647) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.879, test=0.637) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.628) total time=   0.9s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.929, test=0.633) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.931, test=0.638) total time=   0.8s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.926, test=0.635) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.925, test=0.639) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.926, test=0.653) total time=   0.7s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.929, test=0.615) total time=   0.8s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.938, test=0.624) total time=   0.7s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.645) total time=   0.7s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.924, test=0.639) total time=   0.7s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.973, test=0.617) total time=   1.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.976, test=0.629) total time=   2.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.973, test=0.637) total time=   1.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.633) total time=   1.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.640) total time=   1.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.976, test=0.645) total time=   1.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.612) total time=   1.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.977, test=0.617) total time=   1.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.642) total time=   1.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.636) total time=   1.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.637) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.626) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.637) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.662) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.642) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.651) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.645) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.632) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.654) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.651) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.653) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.664) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.627) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.648) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.642) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.636) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.654) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.667) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.628) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.647) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.642) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.650) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.640) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.655) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.651) total time=   0.7s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.657) total time=   0.7s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.670) total time=   0.7s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.629) total time=   0.7s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.648) total time=   0.6s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.658) total time=   0.7s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.644) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.633) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.633) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.648) total time=   0.6s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.826, test=0.640) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.652) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.661) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.620) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.644) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.649) total time=   1.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.642) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.843, test=0.635) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.848, test=0.632) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.650) total time=   1.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.642) total time=   0.9s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.653) total time=   0.9s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.663) total time=   0.9s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.847, test=0.621) total time=   0.9s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.641) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.851, test=0.652) total time=   0.9s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.643) total time=   0.9s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.635) total time=   1.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.875, test=0.640) total time=   1.5s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.873, test=0.650) total time=   1.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.641) total time=   1.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.651) total time=   1.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.665) total time=   1.5s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.875, test=0.623) total time=   1.9s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.871, test=0.637) total time=   1.8s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.876, test=0.652) total time=   1.9s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.641) total time=   1.9s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.630) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.650) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.665) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.645) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.649) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.639) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.630) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.650) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.669) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.648) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.636) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.653) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.632) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.652) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.661) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.650) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.650) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.638) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.658) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.652) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.668) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.649) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.660) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.644) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.652) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.642) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.659) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.648) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.658) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.671) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.716, test=0.631) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.650) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.665) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.649) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.650) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.646) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.744, test=0.660) total time=   0.6s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.652) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.658) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.672) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.637) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.649) total time=   0.6s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.664) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.651) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.878, test=0.628) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.883, test=0.634) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.883, test=0.646) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.875, test=0.636) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.878, test=0.649) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.879, test=0.651) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.884, test=0.615) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.887, test=0.632) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.884, test=0.652) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.878, test=0.637) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.925, test=0.623) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.927, test=0.634) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.923, test=0.641) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.636) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.922, test=0.641) total time=   0.7s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.931, test=0.648) total time=   0.7s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.931, test=0.619) total time=   0.7s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.929, test=0.628) total time=   0.7s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.933, test=0.650) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.920, test=0.636) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.614) total time=   1.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.967, test=0.631) total time=   1.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.640) total time=   1.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.634) total time=   1.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.637) total time=   1.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.974, test=0.641) total time=   1.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.973, test=0.620) total time=   1.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.618) total time=   1.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.644) total time=   1.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.965, test=0.634) total time=   1.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.637) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.646) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.648) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.626) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.662) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.643) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.650) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.646) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.632) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.650) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.653) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.627) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.643) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.647) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.636) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.667) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.628) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.656) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.643) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.639) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.655) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.651) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.655) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.669) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.629) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.648) total time=   0.6s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.658) total time=   0.7s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.644) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.641) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.636) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.649) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.641) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.653) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.660) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.621) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.644) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.650) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.646) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.640) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.638) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.649) total time=   1.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.645) total time=   0.9s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.654) total time=   1.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.664) total time=   0.9s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.623) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.642) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.656) total time=   0.9s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.645) total time=   0.9s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.640) total time=   2.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.644) total time=   1.8s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.647) total time=   1.8s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.644) total time=   1.6s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.652) total time=   2.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.666) total time=   1.9s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.622) total time=   2.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.641) total time=   1.6s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.656) total time=   1.7s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.642) total time=   1.7s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.631) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.649) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.643) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.647) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.638) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.629) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.655) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.650) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.669) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.643) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.653) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.671) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.630) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.649) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.638) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.659) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.652) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.629) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.647) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.658) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.643) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.653) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.641) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.659) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.650) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.672) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.630) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.649) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.664) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.648) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.653) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.645) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.661) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.655) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.660) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.672) total time=   0.7s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.636) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.647) total time=   0.6s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.667) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.650) total time=   0.7s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.637) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.639) total time=   0.7s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.648) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.637) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.863, test=0.654) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.655) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.617) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.642) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.651) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.863, test=0.639) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.634) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.641) total time=   1.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.903, test=0.648) total time=   1.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.906, test=0.644) total time=   1.8s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.650) total time=   1.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.658) total time=   1.5s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.620) total time=   1.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.637) total time=   1.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.654) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.900, test=0.638) total time=   0.7s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.950, test=0.622) total time=   1.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.948, test=0.638) total time=   1.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.645) total time=   2.8s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.643) total time=   1.7s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.647) total time=   1.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.649) total time=   1.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.621) total time=   1.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.629) total time=   1.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.651) total time=   1.6s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.942, test=0.631) total time=   1.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.651) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.652) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.654) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.670) total time=   0.6s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.657) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.640) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.672) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.629) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.652) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.640) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.659) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.673) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.631) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.653) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.664) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.654) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.641) total time=   0.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.661) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.655) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.661) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.676) total time=   0.6s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.632) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.653) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.665) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.655) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.649) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.638) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.655) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.646) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.654) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.668) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.834, test=0.627) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.646) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.836, test=0.653) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.644) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.648) total time=   0.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.640) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.659) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.649) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.661) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.669) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.630) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.645) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.661) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.647) total time=   0.9s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.645) total time=   1.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.637) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.658) total time=   1.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.653) total time=   1.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.659) total time=   1.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.668) total time=   1.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.630) total time=   1.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.646) total time=   1.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.660) total time=   1.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.651) total time=   1.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.653) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.656) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.644) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.634) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   2.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.649) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.637) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.655) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.669) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.654) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.698, test=0.643) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.662) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.654) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.676) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.634) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.652) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.666) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.657) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.645) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.663) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.654) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.678) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.637) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.651) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.668) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.653) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.655) total time=   0.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.646) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.662) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.654) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.661) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.679) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.639) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.652) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.667) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.656) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.648) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.637) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.653) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.639) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.650) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.667) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.876, test=0.628) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.639) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.646) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.640) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.639) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.633) total time=   0.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.651) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.640) total time=   0.7s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.650) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.668) total time=   0.7s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.629) total time=   0.7s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.636) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.650) total time=   0.7s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.641) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.631) total time=   1.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.626) total time=   1.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.647) total time=   1.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.643) total time=   1.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.647) total time=   1.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.659) total time=   1.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.625) total time=   1.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.630) total time=   1.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.653) total time=   1.7s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.638) total time=   1.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.651) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.652) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.654) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.670) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.657) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.653) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.640) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.659) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.672) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.629) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.652) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.640) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.659) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.673) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.632) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.664) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.655) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.641) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.661) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.654) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.661) total time=   0.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.676) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.632) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.653) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.665) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.654) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.648) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.637) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.658) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.646) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.655) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.668) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.626) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.645) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.833, test=0.653) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.645) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.648) total time=   0.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.639) total time=   0.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.660) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.649) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.661) total time=   0.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.670) total time=   0.6s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.631) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.644) total time=   1.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.661) total time=   2.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.648) total time=   1.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.647) total time=   2.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.636) total time=   2.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.659) total time=   1.7s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.653) total time=   1.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.659) total time=   1.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.669) total time=   1.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.631) total time=   1.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.647) total time=   1.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.660) total time=   1.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.652) total time=   1.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.642) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.656) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.644) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.633) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.649) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.654) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.633) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.652) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.644) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.655) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.676) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.633) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.665) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.656) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.646) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.664) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.654) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.678) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.636) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.652) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.669) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.653) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.656) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.648) total time=   0.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.664) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.655) total time=   0.9s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.661) total time=   0.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.679) total time=   0.8s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.636) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.652) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.668) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.655) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.647) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.635) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.654) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.638) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.652) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.671) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.628) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.639) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.649) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.645) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.642) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.631) total time=   0.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.652) total time=   0.7s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.906, test=0.641) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.653) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.669) total time=   0.7s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.628) total time=   0.7s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.638) total time=   0.7s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.654) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.640) total time=   0.9s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.635) total time=   1.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.626) total time=   1.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.649) total time=   1.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.640) total time=   1.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.949, test=0.649) total time=   1.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.660) total time=   1.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.627) total time=   1.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.631) total time=   1.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.650) total time=   1.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.639) total time=   1.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.628) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.629) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.652) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.645) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.631) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.669) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.657) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.651) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.640) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.660) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.672) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.629) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.661) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.653) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.640) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.660) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.673) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.632) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.664) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time=   0.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.641) total time=   0.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.661) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.675) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.633) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.653) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.665) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.655) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.651) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.638) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.657) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.648) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.656) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.671) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.630) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.646) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.656) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.648) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.651) total time=   0.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.816, test=0.640) total time=   0.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.659) total time=   1.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.650) total time=   1.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.661) total time=   1.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.671) total time=   1.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.632) total time=   1.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.647) total time=   1.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.663) total time=   1.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.652) total time=   1.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.649) total time=   1.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.637) total time=   1.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.662) total time=   1.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.654) total time=   1.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.659) total time=   1.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.670) total time=   1.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.632) total time=   1.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.648) total time=   1.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.662) total time=   1.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.655) total time=   1.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.642) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.649) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.644) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.634) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.657) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.649) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.650) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.637) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.654) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.654) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.643) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.655) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.675) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.634) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.651) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.663) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.656) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.646) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.663) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.655) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.677) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.635) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.652) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.666) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.653) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.657) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.645) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.664) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.655) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.663) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.677) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.636) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.652) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.666) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.658) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.650) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.846, test=0.638) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.656) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.645) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.655) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.668) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.630) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.643) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.654) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.643) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.886, test=0.648) total time=   0.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.636) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.891, test=0.653) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.885, test=0.645) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.884, test=0.657) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.885, test=0.667) total time=   0.6s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.634) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.642) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.654) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.885, test=0.644) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.929, test=0.641) total time=   1.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.632) total time=   1.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.650) total time=   1.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.930, test=0.649) total time=   1.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.928, test=0.650) total time=   1.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.660) total time=   1.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.634) total time=   1.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.636) total time=   1.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.653) total time=   1.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.930, test=0.644) total time=   1.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.637) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.626) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.638) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.662) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.642) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.650) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.646) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.632) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.654) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.651) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.653) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.664) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.627) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.648) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.642) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.648) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.637) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.654) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.667) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.628) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.642) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.649) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.640) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.655) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.652) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.656) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.670) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.630) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.648) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.658) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.643) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.632) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.635) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.648) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.641) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.652) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.660) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.620) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.643) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.839, test=0.649) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.642) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.635) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.849, test=0.633) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.847, test=0.649) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.644) total time=   0.7s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.653) total time=   0.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.662) total time=   0.7s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.848, test=0.622) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.640) total time=   0.7s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.852, test=0.652) total time=   0.7s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.843, test=0.643) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.635) total time=   1.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.878, test=0.640) total time=   1.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.876, test=0.650) total time=   1.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.642) total time=   1.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.652) total time=   1.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.872, test=0.664) total time=   1.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.877, test=0.622) total time=   1.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.874, test=0.636) total time=   1.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.878, test=0.651) total time=   1.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.871, test=0.640) total time=   1.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.630) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.650) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.645) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.647) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.630) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.649) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.667) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.629) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.643) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.635) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.632) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.651) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.662) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.649) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.639) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.658) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.653) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.668) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.648) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.658) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.644) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.651) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.714, test=0.644) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.660) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.653) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.659) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.673) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.714, test=0.632) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.714, test=0.651) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.661) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.647) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.650) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.646) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.662) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.656) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.657) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.675) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.641) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.753, test=0.648) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.663) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.649) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.882, test=0.634) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.888, test=0.628) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.887, test=0.643) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.878, test=0.640) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.880, test=0.647) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.879, test=0.651) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.885, test=0.614) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.888, test=0.630) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.885, test=0.652) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.879, test=0.635) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.927, test=0.626) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.935, test=0.630) total time=   0.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.642) total time=   0.7s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.927, test=0.639) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.922, test=0.642) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.653) total time=   0.7s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.937, test=0.613) total time=   0.7s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.937, test=0.624) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.649) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.924, test=0.636) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.616) total time=   1.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.974, test=0.629) total time=   1.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.975, test=0.641) total time=   1.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.974, test=0.634) total time=   1.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.636) total time=   1.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.977, test=0.642) total time=  12.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.979, test=0.609) total time=   2.7s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.977, test=0.612) total time=   2.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.649) total time=   2.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.633) total time=   2.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.637) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.626) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time=   0.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time=   0.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.7s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   1.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time=   1.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.662) total time=   1.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.642) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.651) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time=   1.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.645) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.632) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.655) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.651) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.653) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.664) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.627) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.647) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.643) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.648) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.637) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.654) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.667) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.628) total time=   1.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.647) total time=   0.7s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.656) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.643) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.649) total time=   1.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.640) total time=   1.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.656) total time=   1.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.652) total time=   1.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.656) total time=   1.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.670) total time=   1.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.629) total time=   1.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.648) total time=   1.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.658) total time=   1.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.644) total time=   1.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.635) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.634) total time=   1.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.649) total time=   1.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.826, test=0.639) total time=   1.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.653) total time=   0.8s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.660) total time=   0.8s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.620) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.644) total time=   0.7s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.837, test=0.649) total time=   0.7s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.643) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.637) total time=   1.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.847, test=0.632) total time=   1.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.650) total time=   1.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.643) total time=   1.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.654) total time=   1.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.662) total time=   1.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.620) total time=   1.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.641) total time=   1.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.850, test=0.654) total time=   1.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.842, test=0.642) total time=   1.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.637) total time=   2.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.875, test=0.638) total time=   2.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.873, test=0.649) total time=   2.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.641) total time=   2.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.652) total time=   1.9s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.664) total time=   2.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.875, test=0.623) total time=   1.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.872, test=0.639) total time=  48.9s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.876, test=0.653) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.640) total time=   0.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.630) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.650) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.665) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.645) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.647) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.646) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.630) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.650) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.669) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.629) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.643) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.636) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.653) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.632) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.653) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.650) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.638) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.659) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.653) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.668) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.632) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.660) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.644) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.652) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.643) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.660) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.652) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.661) total time=   0.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.671) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.716, test=0.633) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.650) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.664) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.649) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.653) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.647) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.661) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.751, test=0.653) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.658) total time=   0.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.671) total time=   1.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.751, test=0.637) total time=   1.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.647) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.665) total time=   1.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.651) total time=   1.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.880, test=0.632) total time=   1.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.885, test=0.633) total time=   1.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.887, test=0.644) total time=   1.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.877, test=0.639) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.879, test=0.649) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.877, test=0.653) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.886, test=0.618) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.885, test=0.634) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.883, test=0.649) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.880, test=0.640) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.926, test=0.627) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.928, test=0.635) total time=   0.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.927, test=0.643) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.922, test=0.637) total time=   0.7s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.924, test=0.647) total time=   0.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.925, test=0.652) total time=   0.7s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.620) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.929, test=0.633) total time=   0.7s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.925, test=0.649) total time=   0.7s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.643) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.619) total time=   1.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.626) total time=   1.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.639) total time=   1.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.973, test=0.635) total time=   1.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.967, test=0.642) total time=   1.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.643) total time=   1.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.977, test=0.620) total time=   1.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.975, test=0.625) total time=   1.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.644) total time=   1.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.638) total time=   1.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.638) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.646) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.625) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.662) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.642) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.651) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.645) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.631) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.650) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.653) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.664) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.627) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.647) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.643) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.647) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.636) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.667) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.627) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.656) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.643) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.649) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.639) total time=   0.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.655) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.651) total time=   0.7s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.655) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.670) total time=   0.6s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.629) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.648) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.658) total time=   0.7s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.644) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.640) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.636) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.649) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.641) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.653) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.659) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.621) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.643) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.648) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.645) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.641) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.638) total time=   0.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.650) total time=   1.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.645) total time=   0.9s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.656) total time=   1.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.663) total time=   0.8s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.624) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.642) total time=   0.9s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.654) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.644) total time=   0.9s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.641) total time=   1.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.644) total time=   1.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.649) total time=   1.9s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.644) total time=   1.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.853, test=0.652) total time=   1.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.666) total time=   1.7s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.623) total time=   1.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.640) total time=   2.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.861, test=0.654) total time=   2.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.642) total time=   3.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.631) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.649) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.646) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.629) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.650) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.670) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.658) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.642) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.631) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.651) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.661) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.649) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.637) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.657) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.652) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.631) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.649) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.660) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.653) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.644) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.660) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.651) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.672) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.632) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.652) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.664) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.649) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.654) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.648) total time=   0.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.662) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.655) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.662) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.674) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.640) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.651) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.666) total time=   0.7s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.654) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.865, test=0.641) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.640) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.647) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.865, test=0.635) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.861, test=0.651) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.862, test=0.656) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.621) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.639) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.648) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.637) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.636) total time=   0.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.644) total time=   0.7s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.902, test=0.649) total time=   0.7s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.639) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.647) total time=   0.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.903, test=0.658) total time=   0.8s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.620) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.636) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.649) total time=   0.7s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.899, test=0.639) total time=   0.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.632) total time=   1.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.643) total time=   1.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.646) total time=   1.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.640) total time=   1.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.944, test=0.644) total time=   1.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.653) total time=   1.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.619) total time=   1.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.632) total time=   1.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.949, test=0.649) total time=   1.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.639) total time=   1.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.648) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.655) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.651) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.648) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.631) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.650, test=0.669) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.625) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.652) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.640) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.661) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.652) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.660) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.671) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.629) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.661) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.652) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.640) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.660) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.662) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.673) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.632) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.664) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.654) total time=   0.6s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.640) total time=   0.8s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.7s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time=   0.7s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.661) total time=   0.7s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.676) total time=   0.7s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.632) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.653) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.665) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.655) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.653) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.640) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.661) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.647) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.657) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.669) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.628) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.646) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.655) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.650) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.653) total time=   0.6s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.807, test=0.640) total time=   0.7s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.807, test=0.662) total time=   0.7s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.806, test=0.650) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.809, test=0.661) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.668) total time=   0.7s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.809, test=0.632) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.809, test=0.648) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.662) total time=   0.7s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.651) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.835, test=0.649) total time=   1.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.834, test=0.639) total time=   1.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.834, test=0.662) total time=   1.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.833, test=0.653) total time=   1.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.836, test=0.659) total time=   1.6s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.835, test=0.670) total time=   1.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.835, test=0.632) total time=   1.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.836, test=0.649) total time=   1.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.837, test=0.662) total time=   1.5s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.834, test=0.654) total time=   1.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.644) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.633) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.637) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.654) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.662) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.671) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.654) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.644) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.663) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.655) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.661) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.676) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.635) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.652) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.664) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.656) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.645) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.663) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.656) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.663) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.678) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.635) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.651) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.666) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.651) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.655) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.647) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.664) total time=   0.6s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.657) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.663) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.676) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.637) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.649) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.665) total time=   0.6s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.655) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.648) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.840, test=0.640) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.844, test=0.656) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.840, test=0.643) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.657) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.840, test=0.671) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.842, test=0.630) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.634) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.846, test=0.653) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.843, test=0.643) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.648) total time=   0.6s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.878, test=0.639) total time=   0.6s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.883, test=0.658) total time=   0.6s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.877, test=0.642) total time=   0.7s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.657) total time=   0.7s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.669) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.634) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.632) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.882, test=0.655) total time=   0.6s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.880, test=0.643) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.639) total time=   1.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.633) total time=   1.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.654) total time=   1.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.643) total time=   1.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.930, test=0.652) total time=   1.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.661) total time=   1.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.633) total time=   1.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.932, test=0.627) total time=   1.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.651) total time=   1.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.641) total time=   1.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.648) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.655) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.651) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.648) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.631) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.654) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.650, test=0.669) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.625) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.652) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.640) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.661) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.652) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.660) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.671) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.628) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.662) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.653) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.640) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.660) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.662) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.673) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.632) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.664) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.655) total time=   0.7s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.640) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.661) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.675) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.632) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.653) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.665) total time=   0.6s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.655) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.653) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.640) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.800, test=0.661) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.800, test=0.648) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.800, test=0.657) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.670) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.628) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.646) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.653) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.650) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.805, test=0.653) total time=   0.6s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.804, test=0.641) total time=   0.7s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.805, test=0.661) total time=   0.7s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.804, test=0.650) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.806, test=0.661) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.669) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.807, test=0.631) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.807, test=0.648) total time=   0.7s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.659) total time=   0.7s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.652) total time=   0.7s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.832, test=0.650) total time=   1.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.639) total time=   1.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.832, test=0.663) total time=   1.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.654) total time=   1.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.833, test=0.659) total time=   1.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.832, test=0.671) total time=   1.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.832, test=0.633) total time=   1.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.833, test=0.649) total time=   1.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.834, test=0.661) total time=   1.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.832, test=0.655) total time=   1.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.649) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.644) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.633) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.650) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.636) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.654) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.661) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.660) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.654) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.643) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.663) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.655) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.661) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.676) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.634) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.652) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.664) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.656) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.645) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.663) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.656) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.663) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.678) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.635) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.651) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.666) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.652) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.654) total time=   0.6s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.648) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.663) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.658) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.662) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.675) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.637) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.650) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.665) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.654) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.839, test=0.653) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.837, test=0.641) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.656) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.641) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.839, test=0.653) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.839, test=0.671) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.628) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.636) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.843, test=0.652) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.643) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.646) total time=   0.7s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.638) total time=   0.6s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.881, test=0.656) total time=   0.7s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.874, test=0.641) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.655) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.668) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.877, test=0.632) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.635) total time=   0.7s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.878, test=0.656) total time=   0.6s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.877, test=0.641) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.641) total time=   1.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.932, test=0.631) total time=   1.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.649) total time=   1.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.929, test=0.642) total time=   1.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.928, test=0.651) total time=   1.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.659) total time=   1.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.932, test=0.633) total time=   1.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.932, test=0.629) total time=   1.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.656) total time=   1.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.639) total time=   1.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.628) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.626) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.655) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.652) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.648) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.649) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.650, test=0.669) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.652) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.640) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.660) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.652) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.662) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.671) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.629) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.651) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.662) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.650) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.653) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.640) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.660) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.652) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.663) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.672) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.632) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.652) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.664) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.651) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.655) total time=   0.6s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.640) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.662) total time=   0.6s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.654) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.663) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.675) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.633) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.653) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.666) total time=   0.6s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.655) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.783, test=0.654) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.783, test=0.639) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.783, test=0.661) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.649) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.784, test=0.657) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.669) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.630) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.647) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.656) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.650) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.790, test=0.653) total time=   0.7s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.788, test=0.640) total time=   0.7s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.789, test=0.662) total time=   0.6s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.788, test=0.649) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.789, test=0.661) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.792, test=0.670) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.789, test=0.632) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.791, test=0.649) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.792, test=0.663) total time=   0.7s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.792, test=0.653) total time=   0.7s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.652) total time=   1.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.813, test=0.640) total time=   1.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.813, test=0.664) total time=   1.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.814, test=0.654) total time=   1.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.813, test=0.660) total time=   1.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.814, test=0.671) total time=   1.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.813, test=0.632) total time=   1.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.651) total time=   1.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.662) total time=   1.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.814, test=0.655) total time=   1.5s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.631) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.650) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.628) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.633) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.636) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.654) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.654) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.643) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.664) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.653) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.674) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.634) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.652) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.665) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.649) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.657) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.645) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.664) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.654) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.664) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.676) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.637) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.652) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.667) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.652) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.657) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.647) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.667) total time=   0.6s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.723, test=0.656) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.723, test=0.665) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.676) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.638) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.651) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.667) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.655) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.651) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.638) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.655) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.644) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.657) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.672) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.630) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.638) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.824, test=0.658) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.824, test=0.642) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.856, test=0.649) total time=   1.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.856, test=0.638) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.859, test=0.656) total time=   1.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.855, test=0.645) total time=   1.0s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.855, test=0.659) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.856, test=0.670) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.855, test=0.631) total time=   0.8s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.858, test=0.640) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.859, test=0.661) total time=   0.6s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.859, test=0.643) total time=   0.9s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.909, test=0.645) total time=   2.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.633) total time=   2.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.907, test=0.653) total time=   1.9s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.910, test=0.644) total time=   2.0s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.909, test=0.655) total time=   1.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.912, test=0.664) total time=   1.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.910, test=0.630) total time=   1.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.911, test=0.634) total time=   1.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.655) total time=   1.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.910, test=0.643) total time=   1.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.638) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.626) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.646) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.628) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.625) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.645) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time=   0.8s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time=   0.6s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.661) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.643) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.651) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.637) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.645) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.631) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.650) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.652) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.665) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.627) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.649) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.643) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.636) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.655) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.668) total time=   0.7s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.628) total time=   1.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.647) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.656) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.643) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time=   1.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.639) total time=   1.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.655) total time=   1.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.651) total time=   0.8s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.656) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.670) total time=   0.7s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.629) total time=   0.7s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.647) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.658) total time=   0.7s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.644) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.638) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.637) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.646) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.643) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.651) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.660) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.621) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.643) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.825, test=0.648) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.646) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.640) total time=   1.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.636) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.648) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.646) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.655) total time=   0.9s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.664) total time=   1.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.625) total time=   1.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.641) total time=   0.9s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.652) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.644) total time=   0.9s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.640) total time=   1.8s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.864, test=0.642) total time=   1.6s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.648) total time=   1.9s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.645) total time=   1.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.653) total time=   1.7s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.665) total time=   1.8s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.861, test=0.623) total time=   1.8s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.640) total time=   1.9s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.864, test=0.652) total time=   2.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.643) total time=   1.6s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.630) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.649) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.665) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.638) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.628) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.655) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.650) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.669) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.642) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.653) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.671) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.631) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.650) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.639) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.658) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.652) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.668) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.629) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.648) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.658) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.646) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.653) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.642) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.661) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.651) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.659) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.673) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.631) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.651) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.662) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.649) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.744, test=0.653) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.649) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.743, test=0.663) total time=   0.7s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.744, test=0.656) total time=   0.7s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.662) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.674) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.639) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.650) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.664) total time=   0.6s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.744, test=0.649) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.634) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.876, test=0.636) total time=   0.7s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.875, test=0.643) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.635) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.865, test=0.655) total time=   0.7s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.657) total time=   0.7s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.617) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.641) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.652) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.644) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.627) total time=   0.7s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.924, test=0.637) total time=   1.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.641) total time=   0.8s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.640) total time=   0.8s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.654) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.654) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.618) total time=   0.9s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.635) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.919, test=0.654) total time=   1.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.641) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.615) total time=   1.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.631) total time=   1.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.638) total time=   1.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.635) total time=   1.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.650) total time=   1.6s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.645) total time=   1.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.617) total time=   1.7s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.628) total time=   1.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.648) total time=   1.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.638) total time=   1.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.638) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.626) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.646) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.628) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.625) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.645) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.661) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.626) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.643) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.651) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.637) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.645) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.631) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.652) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.665) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.627) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.648) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.643) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.636) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.652) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.655) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.668) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.628) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.647) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.657) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.644) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.648) total time=   0.7s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.639) total time=   0.7s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.655) total time=   0.6s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.651) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.656) total time=   0.7s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.670) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.629) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.648) total time=   0.7s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.658) total time=   0.7s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.644) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.639) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.638) total time=   0.6s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.647) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.643) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.650) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.659) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.621) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.644) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.823, test=0.648) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.646) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.824, test=0.640) total time=   0.9s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.637) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.649) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.825, test=0.646) total time=   0.8s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.654) total time=   1.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.664) total time=   1.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.626) total time=   1.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.642) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.652) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.645) total time=   0.9s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.640) total time=   1.8s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.642) total time=   1.6s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.861, test=0.649) total time=   1.7s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.644) total time=   1.8s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.853, test=0.653) total time=   2.7s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.664) total time=   2.8s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.625) total time=   2.6s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.642) total time=   1.9s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.652) total time=   1.6s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.643) total time=   2.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.641) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.630) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.665) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.638) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.629) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.651) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.668) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.629) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.631) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.661) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.648) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.649) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.637) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.657) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.651) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.656) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.668) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.630) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.648) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.658) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.645) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.652) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.643) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.660) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.652) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.657) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.673) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.631) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.649) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.662) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.651) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.744, test=0.652) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.742, test=0.646) total time=   0.6s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.743, test=0.663) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.743, test=0.654) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.658) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.675) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.637) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.646) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.661) total time=   0.6s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.653) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.862, test=0.638) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.635) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.875, test=0.649) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.865, test=0.638) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.651) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.655) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.622) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.637) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.652) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.639) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.634) total time=   0.8s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.637) total time=   0.8s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.643) total time=   0.7s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.640) total time=   0.8s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.652) total time=   0.7s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.653) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.625) total time=   0.8s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.919, test=0.633) total time=   0.9s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.653) total time=   0.7s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.637) total time=   0.7s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.963, test=0.621) total time=   1.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.628) total time=   1.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.638) total time=   1.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.967, test=0.640) total time=   1.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.649) total time=   1.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.646) total time=   1.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.626) total time=   1.6s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.624) total time=   1.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.648) total time=   1.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.965, test=0.634) total time=   1.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.638) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.626) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.628) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.625) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.646) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.637) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.638) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.661) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.642) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.651) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.646) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.631) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.654) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.653) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.665) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.629) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.649) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.654) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.644) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.648) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.635) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.654) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.652) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.655) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.668) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.628) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.647) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.657) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.645) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time=   0.7s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.638) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.655) total time=   1.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.652) total time=   1.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.656) total time=   0.9s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.671) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.630) total time=   0.8s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.648) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.658) total time=   1.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.645) total time=   2.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.795, test=0.642) total time=   1.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.798, test=0.638) total time=   1.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.650) total time=   2.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.797, test=0.646) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.650) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.798, test=0.660) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.796, test=0.622) total time=   1.6s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.798, test=0.646) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.646) total time=   0.7s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.648) total time=   0.7s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.643) total time=   1.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.815, test=0.637) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.650) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.646) total time=   1.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.813, test=0.654) total time=   1.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.813, test=0.664) total time=   1.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.812, test=0.625) total time=   0.8s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.815, test=0.645) total time=   0.9s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.653) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.814, test=0.645) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.837, test=0.644) total time=   1.6s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.642) total time=   1.7s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.650) total time=   1.7s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.836, test=0.646) total time=   1.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.837, test=0.655) total time=   1.6s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.840, test=0.665) total time=   1.8s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.625) total time=   1.6s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.644) total time=   1.8s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.653) total time=   1.6s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.644) total time=   1.7s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.630) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.665) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.627) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.638) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.631) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.649) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.669) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.642) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.637) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.652) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.630) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.648) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.648) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.637) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.657) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.652) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.655) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.667) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.629) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.646) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.658) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.646) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.651) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.643) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.661) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.650) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.659) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.670) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.631) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.649) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.662) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.651) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.651) total time=   0.6s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.644) total time=   0.6s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.663) total time=   0.6s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.654) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.660) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.673) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.742, test=0.638) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.645) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.663) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.653) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.642) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.639) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.859, test=0.649) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.641) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.847, test=0.651) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.655) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.623) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.637) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.648) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.646) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.891, test=0.634) total time=   0.7s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.642) total time=   0.8s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.646) total time=   0.8s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.891, test=0.645) total time=   0.7s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.893, test=0.651) total time=   0.7s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.896, test=0.656) total time=   0.7s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.621) total time=   0.8s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.637) total time=   1.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.652) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.893, test=0.645) total time=   0.9s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.625) total time=   2.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.948, test=0.639) total time=   1.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.640) total time=   1.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.643) total time=   1.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.646) total time=   1.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.651) total time=   1.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.624) total time=   1.6s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.625) total time=   1.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.647) total time=   1.4s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.644) total time=   1.4s

C:\Users\woowe\anaconda\Lib\site-packages\numpy\ma\core.py:2820: RuntimeWarning: invalid value encountered in cast
  _data = np.array(data, dtype=dtype, copy=copy,
Out[141]:
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None, device=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False, eval_metric=None,
                                     feature_types=None, gamma=None,
                                     grow_policy=None, importance_ty...
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     multi_strategy=None, n_estimators=None,
                                     n_jobs=None, num_parallel_tree=None,
                                     random_state=42, ...),
             param_grid={'alpha': [0, 0.1, 1], 'colsample_bytree': [0.3, 0.7],
                         'lambda': [0, 0.1, 1], 'learning_rate': [0.01, 0.05],
                         'max_depth': [2, 5, 10],
                         'n_estimators': [50, 100, 200]},
             return_train_score=True, scoring='roc_auc', verbose=4)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None, device=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False, eval_metric=None,
                                     feature_types=None, gamma=None,
                                     grow_policy=None, importance_ty...
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     multi_strategy=None, n_estimators=None,
                                     n_jobs=None, num_parallel_tree=None,
                                     random_state=42, ...),
             param_grid={'alpha': [0, 0.1, 1], 'colsample_bytree': [0.3, 0.7],
                         'lambda': [0, 0.1, 1], 'learning_rate': [0.01, 0.05],
                         'max_depth': [2, 5, 10],
                         'n_estimators': [50, 100, 200]},
             return_train_score=True, scoring='roc_auc', verbose=4)
XGBClassifier(alpha=1, base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.3, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, lambda=1, learning_rate=0.05,
              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=5, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=200, n_jobs=None, ...)
XGBClassifier(alpha=1, base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.3, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, lambda=1, learning_rate=0.05,
              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=5, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=200, n_jobs=None, ...)
In [142]:
report_GridSearchCV_results(grid_search_xgb)

- Best combination of hyperparameters:
 {'alpha': 1, 'colsample_bytree': 0.3, 'lambda': 1, 'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 200} 

- Best mean_test_score:
 0.6578152413134496 

- Score by fold for best estimator:
 [0.6567652743652744, 0.6472609336609336, 0.6667059787059788, 0.6559855855855855, 0.6648111384111384, 0.6761212121212121, 0.638134414012138, 0.6507394945651604, 0.6667289026550527, 0.6548994790520215] 

- Top 10 hyperparameter combinations by mean_test_score:
mean_test_score param_colsample_bytree param_n_estimators param_max_depth param_alpha param_lambda param_learning_rate
rank_test_score
1 0.657815 0.3 200 5 1.0 1.0 0.05
2 0.657519 0.3 200 5 0.0 1.0 0.05
3 0.657363 0.3 200 5 0.0 0.0 0.05
4 0.657353 0.3 200 5 0.1 0.1 0.05
5 0.657237 0.3 200 5 0.0 0.1 0.05
6 0.657202 0.3 200 5 0.1 1.0 0.05
7 0.657145 0.3 200 5 0.1 0.0 0.05
8 0.657091 0.3 100 5 0.1 0.1 0.05
9 0.656973 0.3 100 5 0.0 0.1 0.05
10 0.656893 0.3 100 5 0.1 0.0 0.05
In [143]:
compare_performance(grid_search_xgb)
Out[143]:
train_AUC val_AUC
1 0.649362 0.644820
2 0.650011 0.645682
3 0.652294 0.646945
4 0.684376 0.652973
5 0.686990 0.653782
6 0.694554 0.655196
7 0.831597 0.648039
8 0.840301 0.650964
9 0.868222 0.650752
10 0.652286 0.647157
Mean 0.720999 0.649631
In [144]:
best_model_xgb=grid_search_xgb.best_estimator_
In [145]:
plot_feature_importance_chart(best_model_xgb, X_train, y_train, cv, "XGBoost")
No description has been provided for this image
In [146]:
evaluate_model(best_model_xgb, X_test, y_test)

Test AUC: 0.66
Accuracy: 0.62
Confusion Matrix:
[[2944 1056]
 [1821 1679]]
No description has been provided for this image 
Classification Report:
              precision    recall  f1-score   support

           0       0.62      0.74      0.67      4000
           1       0.61      0.48      0.54      3500

    accuracy                           0.62      7500
   macro avg       0.62      0.61      0.61      7500
weighted avg       0.62      0.62      0.61      7500
In [147]:
plot_roc_curve(best_model_xgb, X_test, y_test)
No description has been provided for this image
In [ ]: